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mbeyeler
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add additional SVM exercises, fix typos
1 parent 10d9fae commit 78f5f6e

9 files changed

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-662
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‎notebooks/06.03-Additional-SVM-Exercises.ipynb‎

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‎notebooks/10.04-Implementing-AdaBoost.ipynb‎

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‎notebooks/10.05-Combining-Different-Models-Into-a-Voting-Classifier.ipynb‎

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"cells": [
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{
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"metadata": {
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"metadata": {},
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"source": [
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"<!--BOOK_INFORMATION-->\n",
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"<a href=\"https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-opencv\" target=\"_blank\"><img align=\"left\" src=\"data/cover.jpg\" style=\"width: 76px; height: 100px; background: white; padding: 1px; border: 1px solid black; margin-right:10px;\"></a>\n",
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"deletable": true,
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"metadata": {},
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"source": [
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"<!--NAVIGATION-->\n",
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"< [Implementing AdaBoost](10.04-Implementing-AdaBoost.ipynb) | [Contents](../README.md) | [Selecting the Right Model with Hyper-Parameter Tuning](11.00-Selecting-the-Right-Model-with-Hyper-Parameter-Tuning.ipynb) >"
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{
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"cell_type": "markdown",
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"metadata": {
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"source": [
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"# Combining Different Models Into a Voting Classifier\n",
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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{
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.model_selection import train_test_split\n",
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<!--NAVIGATION-->\n",
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"< [Implementing AdaBoost](10.04-Implementing-AdaBoost.ipynb) | [Contents](../README.md) | [Selecting the Right Model with Hyper-Parameter Tuning](11.00-Selecting-the-Right-Model-with-Hyper-Parameter-Tuning.ipynb) >"

‎notebooks/11.00-Selecting-the-Right-Model-with-Hyper-Parameter-Tuning.ipynb‎

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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"deletable": true,
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},
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"metadata": {},
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"source": [
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"<!--BOOK_INFORMATION-->\n",
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"<a href=\"https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-opencv\" target=\"_blank\"><img align=\"left\" src=\"data/cover.jpg\" style=\"width: 76px; height: 100px; background: white; padding: 1px; border: 1px solid black; margin-right:10px;\"></a>\n",
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},
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{
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"cell_type": "markdown",
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"source": [
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"<!--NAVIGATION-->\n",
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"< [Combining Different Models Into a Voting Classifier](10.05-Combining-Different-Models-Into-a-Voting-Classifier.ipynb) | [Contents](../README.md) | [Evaluating a Model](11.01-Evaluating-a-Model.ipynb) >"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"deletable": true,
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"source": [
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"# Selecting the Right Model with Hyper-Parameter Tuning\n",
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"\n",
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},
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"cell_type": "markdown",
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"source": [
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"<!--NAVIGATION-->\n",
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"< [Combining Different Models Into a Voting Classifier](10.05-Combining-Different-Models-Into-a-Voting-Classifier.ipynb) | [Contents](../README.md) | [Evaluating a Model](11.01-Evaluating-a-Model.ipynb) >"
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}
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"nbformat": 4,
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‎notebooks/11.01-Evaluating-a-Model.ipynb‎

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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"deletable": true,
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"editable": true
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},
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"metadata": {},
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"source": [
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"<!--BOOK_INFORMATION-->\n",
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"<a href=\"https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-opencv\" target=\"_blank\"><img align=\"left\" src=\"data/cover.jpg\" style=\"width: 76px; height: 100px; background: white; padding: 1px; border: 1px solid black; margin-right:10px;\"></a>\n",
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},
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{
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"cell_type": "markdown",
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"source": [
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"<!--NAVIGATION-->\n",
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"< [Selecting the Right Model with Hyper-Parameter Tuning](11.00-Selecting-the-Right-Model-with-Hyper-Parameter-Tuning.ipynb) | [Contents](../README.md) | [Understanding Cross-Validation](11.02-Understanding-Cross-Validation-Bootstrapping-and-McNemar's-Test.ipynb) >"
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]
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},
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{
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"cell_type": "markdown",
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"# Evaluating a Model\n",
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"\n",
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"from sklearn.datasets import load_iris\n",
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{
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"knn.train(X, cv2.ml.ROW_SAMPLE, y)\n",
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"X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=37,\n",
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"knn = cv2.ml.KNearest_create()\n",
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"<!--NAVIGATION-->\n",
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"< [Selecting the Right Model with Hyper-Parameter Tuning](11.00-Selecting-the-Right-Model-with-Hyper-Parameter-Tuning.ipynb) | [Contents](../README.md) | [Understanding Cross-Validation](11.02-Understanding-Cross-Validation-Bootstrapping-and-McNemar's-Test.ipynb) >"
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}
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