3_evaluate_algorithms.py

# 1. Load Libraries
from pandas import read_csv
from pandas.plotting import scatter_matrix
from matplotlib import pyplot
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC

# Load the Data
names=['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']
dataset = read_csv('iris.csv', names=names)

# Evaluate Some Algorithms
array = dataset.values
X = array[:, 0:4]
Y = array[:, 4]
X_train, X_validation, Y_train, Y_validation = train_test_split(X, Y, test_size=0.2, random_state=1)

print(X_train.shape)
print(X_validation.shape)

models = []
models.append(('LR', LogisticRegression(solver='liblinear', multi_class='ovr')))
models.append(('LDA', LinearDiscriminantAnalysis()))
models.append(('KNN', KNeighborsClassifier()))
models.append(('CART', DecisionTreeClassifier()))
models.append(('NB', GaussianNB()))
models.append(('SVM', SVC(gamma='auto')))

results = []
names = []
for name, model in models:
  kfold = StratifiedKFold(n_splits=10, random_state=1, shuffle=True)
  cv_results = cross_val_score(model, X_train, Y_train, cv=kfold, scoring='accuracy')
  results.append(cv_results)
  names.append(name)
  print('%s: %f (%f)' % (name, cv_results.mean(), cv_results.std()))

# Compare Algorithms
pyplot.boxplot(results, labels=names)
pyplot.title('Algorithm Comparison')
pyplot.show()

model = SVC(gamma='auto')
model.fit(X_train, Y_train)
predictions = model.predict(X_validation)

print(accuracy_score(Y_validation, predictions))
print(confusion_matrix(Y_validation, predictions))
print(classification_report(Y_validation, predictions))