sklearn

Code:

from sklearn import datasets
from sklearn import cross_validation
from sklearn.naive_bayes import GaussianNB 
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from sklearn import metrics

#Create a classification dataset
X, y = datasets.make_classification(n_samples=1000, n_features=10, 
            n_informative=2, n_redundant=2, n_repeated=0, n_classes=2)

#Split the dataset using 10-fold cross validation
kf = cross_validation.KFold(len(X), n_folds=10, shuffle=True)
for train_index, test_index in kf:
    X_train, y_train = X[train_index], y[train_index]
    X_test, y_test = X[test_index], y[test_index]

#Train the algorithms GaussianNB
clf1 = GaussianNB()
clf1.fit(X_train, y_train)
pred1 = clf1.predict(X_test)
print('pred1:\n', pred1)
print('y_test:\n', y_test)

#Evaluate the cross-validated performance
acc1 = metrics.accuracy_score(y_test, pred1)
print('acc1:\n', acc1)
f11 = metrics.f1_score(y_test, pred1)
print('f11:\n', f11)
auc1 = metrics.roc_auc_score(y_test, pred1)
print('auc1:\n', auc1)

#Train the algorithms SVC
clf2 = SVC(C=1e-01, kernel='rbf', gamma=0.1)
clf2.fit(X_train, y_train)
pred2 = clf2.predict(X_test)
print('pred2:\n', pred2)
print('y_test:\n', y_test)

# Evaluate the cross-validated performance
acc2 = metrics.accuracy_score(y_test, pred2)
print('acc2:\n', acc2)
f12 = metrics.f1_score(y_test, pred2)
print('f12:\n', f12)
auc2 = metrics.roc_auc_score(y_test, pred2)
print('auc2:\n', auc2)

#Train the algorithms RandomForestClassifier
clf3 = RandomForestClassifier(n_estimators=6)
clf3.fit(X_train, y_train)
pred3 = clf3.predict(X_test)
print('pred3:\n', pred3)
print('y_test:\n', y_test)

#Evaluate the cross-validated performance
acc3 = metrics.accuracy_score(y_test, pred3)
print('acc3:\n', acc3)
f13 = metrics.f1_score(y_test, pred3)
print('f13:\n', f13)
auc3 = metrics.roc_auc_score(y_test, pred3)
print('auc3:\n', auc3)