Creating, training, predicting, and evaluating an ensemble

ensemble_hard_voting.py

# Combining the three models into an ensemble
from sklearn.ensemble import VotingClassifier

# The ensemble is a voting classifier that aggregates our three models
voting_clf = VotingClassifier(estimators=[('svm', svm_clf), ('tree', tree_clf), ('log', log_clf)], 
                             voting='hard')

voting_clf.fit(X_train, y_train) # training
y_pred_voting = voting_clf.predict(X_test) # predicting
accuracy_score(y_test, y_pred_voting) # evaluating

# Output of the evaluation: 0.904