notebook_seeds.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "390600dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import balanced_accuracy_score, classification_report\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "aed811af",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('seeds.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "86e4338c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Area</th>\n",
       "      <th>Perimeter</th>\n",
       "      <th>Compactness</th>\n",
       "      <th>Kernel.Length</th>\n",
       "      <th>Kernel.Width</th>\n",
       "      <th>Asymmetry.Coeff</th>\n",
       "      <th>Kernel.Groove</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>199.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>199.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>14.918744</td>\n",
       "      <td>14.595829</td>\n",
       "      <td>0.870811</td>\n",
       "      <td>5.643151</td>\n",
       "      <td>3.265533</td>\n",
       "      <td>3.699217</td>\n",
       "      <td>5.420653</td>\n",
       "      <td>1.994975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.919976</td>\n",
       "      <td>1.310445</td>\n",
       "      <td>0.023320</td>\n",
       "      <td>0.443593</td>\n",
       "      <td>0.378322</td>\n",
       "      <td>1.471102</td>\n",
       "      <td>0.492718</td>\n",
       "      <td>0.813382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10.590000</td>\n",
       "      <td>12.410000</td>\n",
       "      <td>0.808100</td>\n",
       "      <td>4.899000</td>\n",
       "      <td>2.630000</td>\n",
       "      <td>0.765100</td>\n",
       "      <td>4.519000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>12.330000</td>\n",
       "      <td>13.470000</td>\n",
       "      <td>0.857100</td>\n",
       "      <td>5.267000</td>\n",
       "      <td>2.954500</td>\n",
       "      <td>2.570000</td>\n",
       "      <td>5.046000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>14.430000</td>\n",
       "      <td>14.370000</td>\n",
       "      <td>0.873400</td>\n",
       "      <td>5.541000</td>\n",
       "      <td>3.245000</td>\n",
       "      <td>3.631000</td>\n",
       "      <td>5.228000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>17.455000</td>\n",
       "      <td>15.805000</td>\n",
       "      <td>0.886800</td>\n",
       "      <td>6.002000</td>\n",
       "      <td>3.564500</td>\n",
       "      <td>4.799000</td>\n",
       "      <td>5.879000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>21.180000</td>\n",
       "      <td>17.250000</td>\n",
       "      <td>0.918300</td>\n",
       "      <td>6.675000</td>\n",
       "      <td>4.033000</td>\n",
       "      <td>8.315000</td>\n",
       "      <td>6.550000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Area   Perimeter  Compactness  Kernel.Length  Kernel.Width  \\\n",
       "count  199.000000  199.000000   199.000000     199.000000    199.000000   \n",
       "mean    14.918744   14.595829     0.870811       5.643151      3.265533   \n",
       "std      2.919976    1.310445     0.023320       0.443593      0.378322   \n",
       "min     10.590000   12.410000     0.808100       4.899000      2.630000   \n",
       "25%     12.330000   13.470000     0.857100       5.267000      2.954500   \n",
       "50%     14.430000   14.370000     0.873400       5.541000      3.245000   \n",
       "75%     17.455000   15.805000     0.886800       6.002000      3.564500   \n",
       "max     21.180000   17.250000     0.918300       6.675000      4.033000   \n",
       "\n",
       "       Asymmetry.Coeff  Kernel.Groove        Type  \n",
       "count       199.000000     199.000000  199.000000  \n",
       "mean          3.699217       5.420653    1.994975  \n",
       "std           1.471102       0.492718    0.813382  \n",
       "min           0.765100       4.519000    1.000000  \n",
       "25%           2.570000       5.046000    1.000000  \n",
       "50%           3.631000       5.228000    2.000000  \n",
       "75%           4.799000       5.879000    3.000000  \n",
       "max           8.315000       6.550000    3.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cea88fbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 504x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Type'].value_counts().plot(kind='bar', figsize=(7,3))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "31c1b5e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Area</th>\n",
       "      <th>Perimeter</th>\n",
       "      <th>Compactness</th>\n",
       "      <th>Kernel.Length</th>\n",
       "      <th>Kernel.Width</th>\n",
       "      <th>Asymmetry.Coeff</th>\n",
       "      <th>Kernel.Groove</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Area</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.994437</td>\n",
       "      <td>0.609887</td>\n",
       "      <td>0.951126</td>\n",
       "      <td>0.970980</td>\n",
       "      <td>-0.222820</td>\n",
       "      <td>0.862727</td>\n",
       "      <td>-0.340664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perimeter</th>\n",
       "      <td>0.994437</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.531841</td>\n",
       "      <td>0.972944</td>\n",
       "      <td>0.945496</td>\n",
       "      <td>-0.211034</td>\n",
       "      <td>0.889543</td>\n",
       "      <td>-0.322223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Compactness</th>\n",
       "      <td>0.609887</td>\n",
       "      <td>0.531841</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.374011</td>\n",
       "      <td>0.762167</td>\n",
       "      <td>-0.329400</td>\n",
       "      <td>0.227016</td>\n",
       "      <td>-0.539261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kernel.Length</th>\n",
       "      <td>0.951126</td>\n",
       "      <td>0.972944</td>\n",
       "      <td>0.374011</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.862680</td>\n",
       "      <td>-0.169704</td>\n",
       "      <td>0.932124</td>\n",
       "      <td>-0.249422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kernel.Width</th>\n",
       "      <td>0.970980</td>\n",
       "      <td>0.945496</td>\n",
       "      <td>0.762167</td>\n",
       "      <td>0.862680</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.253122</td>\n",
       "      <td>0.748228</td>\n",
       "      <td>-0.421961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asymmetry.Coeff</th>\n",
       "      <td>-0.222820</td>\n",
       "      <td>-0.211034</td>\n",
       "      <td>-0.329400</td>\n",
       "      <td>-0.169704</td>\n",
       "      <td>-0.253122</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.003349</td>\n",
       "      <td>0.573956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kernel.Groove</th>\n",
       "      <td>0.862727</td>\n",
       "      <td>0.889543</td>\n",
       "      <td>0.227016</td>\n",
       "      <td>0.932124</td>\n",
       "      <td>0.748228</td>\n",
       "      <td>-0.003349</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.035508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Type</th>\n",
       "      <td>-0.340664</td>\n",
       "      <td>-0.322223</td>\n",
       "      <td>-0.539261</td>\n",
       "      <td>-0.249422</td>\n",
       "      <td>-0.421961</td>\n",
       "      <td>0.573956</td>\n",
       "      <td>0.035508</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Area  Perimeter  Compactness  Kernel.Length  \\\n",
       "Area             1.000000   0.994437     0.609887       0.951126   \n",
       "Perimeter        0.994437   1.000000     0.531841       0.972944   \n",
       "Compactness      0.609887   0.531841     1.000000       0.374011   \n",
       "Kernel.Length    0.951126   0.972944     0.374011       1.000000   \n",
       "Kernel.Width     0.970980   0.945496     0.762167       0.862680   \n",
       "Asymmetry.Coeff -0.222820  -0.211034    -0.329400      -0.169704   \n",
       "Kernel.Groove    0.862727   0.889543     0.227016       0.932124   \n",
       "Type            -0.340664  -0.322223    -0.539261      -0.249422   \n",
       "\n",
       "                 Kernel.Width  Asymmetry.Coeff  Kernel.Groove      Type  \n",
       "Area                 0.970980        -0.222820       0.862727 -0.340664  \n",
       "Perimeter            0.945496        -0.211034       0.889543 -0.322223  \n",
       "Compactness          0.762167        -0.329400       0.227016 -0.539261  \n",
       "Kernel.Length        0.862680        -0.169704       0.932124 -0.249422  \n",
       "Kernel.Width         1.000000        -0.253122       0.748228 -0.421961  \n",
       "Asymmetry.Coeff     -0.253122         1.000000      -0.003349  0.573956  \n",
       "Kernel.Groove        0.748228        -0.003349       1.000000  0.035508  \n",
       "Type                -0.421961         0.573956       0.035508  1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "278cb4bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[['Area', 'Perimeter', 'Compactness', 'Kernel.Length', 'Kernel.Width', 'Asymmetry.Coeff', 'Kernel.Groove']]\n",
    "y = df['Type']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "24f42a8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=50, test_size = 0.5, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "59caa91e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2    34\n",
       " 1    33\n",
       " 3    32\n",
       " Name: Type, dtype: int64,\n",
       " 2    34\n",
       " 3    33\n",
       " 1    33\n",
       " Name: Type, dtype: int64)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts(), y_test.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9dd2d49",
   "metadata": {},
   "source": [
    "**DecisionTreeClassifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "21d5b2e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 36 candidates, totalling 108 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_search.py:922: UserWarning: One or more of the test scores are non-finite: [0.87878788 0.87878788 0.67676768 0.88888889 0.87878788 0.67676768\n",
      " 0.90909091 0.88888889 0.64646465 0.88888889 0.88888889 0.64646465\n",
      " 0.88888889 0.88888889 0.64646465 0.88888889 0.91919192 0.67676768\n",
      " 0.87878788 0.88888889 0.67676768 0.88888889 0.91919192 0.64646465\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan]\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, estimator=DecisionTreeClassifier(),\n",
       "             param_grid={'criterion': ['entropy', 'gini', 'log_loss'],\n",
       "                         'max_depth': [5, 10, 15, 20],\n",
       "                         'min_samples_split': [30, 40, 50]},\n",
       "             scoring='accuracy', verbose=1)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = {'min_samples_split': [30, 40, 50], 'max_depth': [5, 10, 15, 20], 'criterion': ['entropy', 'gini', 'log_loss']}\n",
    "grid=GridSearchCV(estimator=DecisionTreeClassifier(), param_grid=param_grid, scoring='accuracy',cv=3, verbose=1, refit=True )\n",
    "grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a368578f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'criterion': 'gini', 'max_depth': 10, 'min_samples_split': 40}\n",
      "DecisionTreeClassifier(max_depth=10, min_samples_split=40)\n",
      "Mean cross-validated score of the best_estimator:  0.9191919191919192\n",
      "The number of cross-validation splits (folds/iterations):  3\n"
     ]
    }
   ],
   "source": [
    "print(grid.best_params_)\n",
    "print(grid.best_estimator_)\n",
    "print('Mean cross-validated score of the best_estimator: ', grid.best_score_)\n",
    "print('The number of cross-validation splits (folds/iterations): ', grid.n_splits_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "937f29d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=10, min_samples_split=40)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = DecisionTreeClassifier(min_samples_split=40,  max_depth=10, criterion = 'gini')\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8b4d1626",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8992869875222816"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "balanced_accuracy_score(y_test, model.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bc181515",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.83      0.88      0.85        33\n",
      "           2       0.97      0.97      0.97        34\n",
      "           3       0.90      0.85      0.88        33\n",
      "\n",
      "    accuracy                           0.90       100\n",
      "   macro avg       0.90      0.90      0.90       100\n",
      "weighted avg       0.90      0.90      0.90       100\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, model.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9aaa742",
   "metadata": {},
   "source": [
    "**LogisticRegression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "644cfd53",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 21 candidates, totalling 63 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 1306, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 443, in _check_solver\n",
      "    raise ValueError(\"Solver %s supports only 'l2' or 'none' penalties, \"\n",
      "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 1306, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 443, in _check_solver\n",
      "    raise ValueError(\"Solver %s supports only 'l2' or 'none' penalties, \"\n",
      "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 1306, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 443, in _check_solver\n",
      "    raise ValueError(\"Solver %s supports only 'l2' or 'none' penalties, \"\n",
      "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 1306, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 443, in _check_solver\n",
      "    raise ValueError(\"Solver %s supports only 'l2' or 'none' penalties, \"\n",
      "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 1306, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 443, in _check_solver\n",
      "    raise ValueError(\"Solver %s supports only 'l2' or 'none' penalties, \"\n",
      "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 1306, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 443, in _check_solver\n",
      "    raise ValueError(\"Solver %s supports only 'l2' or 'none' penalties, \"\n",
      "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 1306, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 443, in _check_solver\n",
      "    raise ValueError(\"Solver %s supports only 'l2' or 'none' penalties, \"\n",
      "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 1306, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 443, in _check_solver\n",
      "    raise ValueError(\"Solver %s supports only 'l2' or 'none' penalties, \"\n",
      "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 1306, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 443, in _check_solver\n",
      "    raise ValueError(\"Solver %s supports only 'l2' or 'none' penalties, \"\n",
      "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 1306, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 443, in _check_solver\n",
      "    raise ValueError(\"Solver %s supports only 'l2' or 'none' penalties, \"\n",
      "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 1306, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 443, in _check_solver\n",
      "    raise ValueError(\"Solver %s supports only 'l2' or 'none' penalties, \"\n",
      "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 1306, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 443, in _check_solver\n",
      "    raise ValueError(\"Solver %s supports only 'l2' or 'none' penalties, \"\n",
      "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 1306, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 443, in _check_solver\n",
      "    raise ValueError(\"Solver %s supports only 'l2' or 'none' penalties, \"\n",
      "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 1306, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 443, in _check_solver\n",
      "    raise ValueError(\"Solver %s supports only 'l2' or 'none' penalties, \"\n",
      "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 1306, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 443, in _check_solver\n",
      "    raise ValueError(\"Solver %s supports only 'l2' or 'none' penalties, \"\n",
      "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 1306, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 443, in _check_solver\n",
      "    raise ValueError(\"Solver %s supports only 'l2' or 'none' penalties, \"\n",
      "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 1306, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 443, in _check_solver\n",
      "    raise ValueError(\"Solver %s supports only 'l2' or 'none' penalties, \"\n",
      "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 1306, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 443, in _check_solver\n",
      "    raise ValueError(\"Solver %s supports only 'l2' or 'none' penalties, \"\n",
      "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 1306, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 443, in _check_solver\n",
      "    raise ValueError(\"Solver %s supports only 'l2' or 'none' penalties, \"\n",
      "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 1306, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 443, in _check_solver\n",
      "    raise ValueError(\"Solver %s supports only 'l2' or 'none' penalties, \"\n",
      "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 1306, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 443, in _check_solver\n",
      "    raise ValueError(\"Solver %s supports only 'l2' or 'none' penalties, \"\n",
      "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 1306, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 443, in _check_solver\n",
      "    raise ValueError(\"Solver %s supports only 'l2' or 'none' penalties, \"\n",
      "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 1306, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 443, in _check_solver\n",
      "    raise ValueError(\"Solver %s supports only 'l2' or 'none' penalties, \"\n",
      "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 1306, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 443, in _check_solver\n",
      "    raise ValueError(\"Solver %s supports only 'l2' or 'none' penalties, \"\n",
      "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 1306, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 443, in _check_solver\n",
      "    raise ValueError(\"Solver %s supports only 'l2' or 'none' penalties, \"\n",
      "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 1306, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 443, in _check_solver\n",
      "    raise ValueError(\"Solver %s supports only 'l2' or 'none' penalties, \"\n",
      "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 1306, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 443, in _check_solver\n",
      "    raise ValueError(\"Solver %s supports only 'l2' or 'none' penalties, \"\n",
      "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 1306, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 443, in _check_solver\n",
      "    raise ValueError(\"Solver %s supports only 'l2' or 'none' penalties, \"\n",
      "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 1306, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 443, in _check_solver\n",
      "    raise ValueError(\"Solver %s supports only 'l2' or 'none' penalties, \"\n",
      "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 1306, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 443, in _check_solver\n",
      "    raise ValueError(\"Solver %s supports only 'l2' or 'none' penalties, \"\n",
      "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 1306, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 443, in _check_solver\n",
      "    raise ValueError(\"Solver %s supports only 'l2' or 'none' penalties, \"\n",
      "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 1306, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 443, in _check_solver\n",
      "    raise ValueError(\"Solver %s supports only 'l2' or 'none' penalties, \"\n",
      "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 1306, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 443, in _check_solver\n",
      "    raise ValueError(\"Solver %s supports only 'l2' or 'none' penalties, \"\n",
      "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 1306, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 443, in _check_solver\n",
      "    raise ValueError(\"Solver %s supports only 'l2' or 'none' penalties, \"\n",
      "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 1306, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 443, in _check_solver\n",
      "    raise ValueError(\"Solver %s supports only 'l2' or 'none' penalties, \"\n",
      "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 1306, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 443, in _check_solver\n",
      "    raise ValueError(\"Solver %s supports only 'l2' or 'none' penalties, \"\n",
      "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 1306, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 443, in _check_solver\n",
      "    raise ValueError(\"Solver %s supports only 'l2' or 'none' penalties, \"\n",
      "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 1306, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 443, in _check_solver\n",
      "    raise ValueError(\"Solver %s supports only 'l2' or 'none' penalties, \"\n",
      "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 1306, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 443, in _check_solver\n",
      "    raise ValueError(\"Solver %s supports only 'l2' or 'none' penalties, \"\n",
      "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 1306, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 443, in _check_solver\n",
      "    raise ValueError(\"Solver %s supports only 'l2' or 'none' penalties, \"\n",
      "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 1306, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 443, in _check_solver\n",
      "    raise ValueError(\"Solver %s supports only 'l2' or 'none' penalties, \"\n",
      "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 1306, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py\", line 443, in _check_solver\n",
      "    raise ValueError(\"Solver %s supports only 'l2' or 'none' penalties, \"\n",
      "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_search.py:922: UserWarning: One or more of the test scores are non-finite: [       nan 0.94949495        nan        nan 0.95959596        nan\n",
      "        nan 0.95959596        nan        nan 0.94949495        nan\n",
      "        nan 0.95959596        nan        nan 0.93939394        nan\n",
      "        nan 0.94949495        nan]\n",
      "  warnings.warn(\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, estimator=LogisticRegression(),\n",
       "             param_grid={'C': [1, 2, 3, 4, 5, 6, 7],\n",
       "                         'penalty': ['l1', 'l2', 'elasticnet']},\n",
       "             scoring='accuracy', verbose=1)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = {'C': [1, 2, 3, 4, 5, 6, 7], 'penalty': ['l1', 'l2', 'elasticnet']}\n",
    "grid=GridSearchCV(estimator=LogisticRegression(), param_grid=param_grid, scoring='accuracy',cv=3, verbose=1, refit=True )\n",
    "grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e76fdca0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 2, 'penalty': 'l2'}\n",
      "LogisticRegression(C=2)\n",
      "Mean cross-validated score of the best_estimator:  0.9595959595959597\n",
      "The number of cross-validation splits (folds/iterations):  3\n"
     ]
    }
   ],
   "source": [
    "print(grid.best_params_)\n",
    "print(grid.best_estimator_)\n",
    "print('Mean cross-validated score of the best_estimator: ', grid.best_score_)\n",
    "print('The number of cross-validation splits (folds/iterations): ', grid.n_splits_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0a071e19",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=2)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LogisticRegression(C = 2, penalty = 'l2')\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b64b6e95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9200831847890671"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "balanced_accuracy_score(y_test, model.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a3e26d1b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.86      0.91      0.88        33\n",
      "           2       0.94      0.91      0.93        34\n",
      "           3       0.97      0.94      0.95        33\n",
      "\n",
      "    accuracy                           0.92       100\n",
      "   macro avg       0.92      0.92      0.92       100\n",
      "weighted avg       0.92      0.92      0.92       100\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, model.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67fdb6e1",
   "metadata": {},
   "source": [
    "**RandomForestClassifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2b4f7e90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 72 candidates, totalling 216 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 598, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 387, in fit\n",
      "    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 169, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 903, in fit\n",
      "    super().fit(\n",
      "  File \"/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 348, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/Users/mihailsidorov/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_search.py:922: UserWarning: One or more of the test scores are non-finite: [0.8989899  0.90909091 0.8989899  0.90909091 0.8989899  0.90909091\n",
      " 0.88888889 0.8989899  0.90909091 0.8989899  0.8989899  0.8989899\n",
      " 0.90909091 0.8989899  0.8989899  0.90909091 0.8989899  0.8989899\n",
      " 0.88888889 0.92929293 0.91919192 0.8989899  0.90909091 0.8989899\n",
      " 0.8989899  0.88888889 0.90909091 0.8989899  0.8989899  0.90909091\n",
      " 0.90909091 0.8989899  0.8989899  0.8989899  0.88888889 0.8989899\n",
      " 0.8989899  0.8989899  0.8989899  0.90909091 0.8989899  0.90909091\n",
      " 0.91919192 0.91919192 0.90909091 0.8989899  0.8989899  0.90909091\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan]\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, estimator=RandomForestClassifier(),\n",
       "             param_grid={'criterion': ['gini', 'entropy', 'log_loss'],\n",
       "                         'max_depth': [5, 10, 15, 20],\n",
       "                         'n_estimators': [50, 60, 70, 80, 90, 100]},\n",
       "             scoring='accuracy', verbose=1)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = {'n_estimators': [50, 60, 70, 80, 90, 100], 'criterion': ['gini', 'entropy', 'log_loss'], 'max_depth': [5, 10, 15, 20]}\n",
    "grid=GridSearchCV(estimator=RandomForestClassifier(), param_grid=param_grid, scoring='accuracy', cv=3, verbose=1, refit=True )\n",
    "grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "69bc01ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'criterion': 'gini', 'max_depth': 20, 'n_estimators': 60}\n",
      "RandomForestClassifier(max_depth=20, n_estimators=60)\n",
      "Mean cross-validated score of the best_estimator:  0.9292929292929294\n",
      "The number of cross-validation splits (folds/iterations):  3\n"
     ]
    }
   ],
   "source": [
    "print(grid.best_params_)\n",
    "print(grid.best_estimator_)\n",
    "print('Mean cross-validated score of the best_estimator: ', grid.best_score_)\n",
    "print('The number of cross-validation splits (folds/iterations): ', grid.n_splits_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d51bb2a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=20, n_estimators=60)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = RandomForestClassifier(criterion = 'gini', max_depth = 20, n_estimators=60)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c8597968",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9096850861556743"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "balanced_accuracy_score(y_test, model.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "dd99ac0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.85      0.88      0.87        33\n",
      "           2       0.94      0.94      0.94        34\n",
      "           3       0.94      0.91      0.92        33\n",
      "\n",
      "    accuracy                           0.91       100\n",
      "   macro avg       0.91      0.91      0.91       100\n",
      "weighted avg       0.91      0.91      0.91       100\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, model.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5461f1a5",
   "metadata": {},
   "source": [
    "**KNeighborsClassifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1d057d61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 20 candidates, totalling 60 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, estimator=KNeighborsClassifier(),\n",
       "             param_grid={'n_neighbors': [1, 2, 3, 4, 5], 'p': [1, 2],\n",
       "                         'weights': ['uniform', 'distance']},\n",
       "             scoring='accuracy', verbose=1)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = {'n_neighbors': [1, 2, 3, 4, 5], 'weights': ['uniform', 'distance'], 'p': [1, 2]}\n",
    "grid=GridSearchCV(estimator=KNeighborsClassifier(), param_grid=param_grid, scoring='accuracy', cv=3, verbose=1, refit=True )\n",
    "grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "66c48572",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_neighbors': 3, 'p': 1, 'weights': 'uniform'}\n",
      "KNeighborsClassifier(n_neighbors=3, p=1)\n",
      "Mean cross-validated score of the best_estimator:  0.9595959595959597\n",
      "The number of cross-validation splits (folds/iterations):  3\n"
     ]
    }
   ],
   "source": [
    "print(grid.best_params_)\n",
    "print(grid.best_estimator_)\n",
    "print('Mean cross-validated score of the best_estimator: ', grid.best_score_)\n",
    "print('The number of cross-validation splits (folds/iterations): ', grid.n_splits_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2501da10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=3, p=1)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = KNeighborsClassifier(n_neighbors=3, weights='uniform', p=1)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "07f4d927",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9004753416518122"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "balanced_accuracy_score(y_test, model.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "cb2ed091",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.83      0.88      0.85        33\n",
      "           2       1.00      0.85      0.92        34\n",
      "           3       0.89      0.97      0.93        33\n",
      "\n",
      "    accuracy                           0.90       100\n",
      "   macro avg       0.91      0.90      0.90       100\n",
      "weighted avg       0.91      0.90      0.90       100\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, model.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c8f56cf",
   "metadata": {},
   "source": [
    "**SVC**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d78848b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 45 candidates, totalling 135 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, estimator=SVC(),\n",
       "             param_grid={'C': [0.1, 0.5, 1, 5, 10, 50, 100, 500, 1000],\n",
       "                         'gamma': [1, 0.1, 0.01, 0.001, 0.0001],\n",
       "                         'kernel': ['rbf']},\n",
       "             scoring='accuracy', verbose=1)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = {'C': [0.1, 0.5, 1, 5, 10, 50, 100, 500, 1000], 'gamma': [1,0.1,0.01,0.001,0.0001], 'kernel': ['rbf']}\n",
    "grid=GridSearchCV(estimator=SVC(), param_grid=param_grid, scoring='accuracy',cv=3, verbose=1, refit=True )\n",
    "grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "32e01bb3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 5, 'gamma': 0.1, 'kernel': 'rbf'}\n",
      "SVC(C=5, gamma=0.1)\n",
      "Mean cross-validated score of the best_estimator:  0.9595959595959597\n",
      "The number of cross-validation splits (folds/iterations):  3\n"
     ]
    }
   ],
   "source": [
    "print(grid.best_params_)\n",
    "print(grid.best_estimator_)\n",
    "print('Mean cross-validated score of the best_estimator: ', grid.best_score_)\n",
    "print('The number of cross-validation splits (folds/iterations): ', grid.n_splits_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "02878932",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=5, gamma=0.1)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=SVC(C=5, gamma = 0.1, kernel='rbf', tol=0.001)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0e02e647",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9001782531194297"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "balanced_accuracy_score(y_test, model.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0ae4d61b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.85      0.85      0.85        33\n",
      "           2       0.97      0.88      0.92        34\n",
      "           3       0.89      0.97      0.93        33\n",
      "\n",
      "    accuracy                           0.90       100\n",
      "   macro avg       0.90      0.90      0.90       100\n",
      "weighted avg       0.90      0.90      0.90       100\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, model.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "50030f85",
   "metadata": {},
   "outputs": [],
   "source": [
    "from joblib import load\n",
    "\n",
    "model = load('rf.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5e6879eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([df[['Area', 'Perimeter', 'Compactness', 'Kernel.Length', 'Kernel.Width', 'Asymmetry.Coeff', 'Kernel.Groove']].iloc[0, :].tolist()])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e4851dee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[15.26, 14.84, 0.871, 5.763, 3.312, 2.221, 5.22]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[df[['Area', 'Perimeter', 'Compactness', 'Kernel.Length', 'Kernel.Width', 'Asymmetry.Coeff', 'Kernel.Groove']].iloc[0, :].tolist()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c2385f3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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