from sklearn.ensemble import RandomForestClassifier

# Initialize a classifier object with default params
classifier = RandomForestClassifier()
classifier.fit(X_train, y_train)

# Make predictions using both train and test set

rf_train_pred = classifier.predict(X_train)
rf_test_pred = classifier.predict(X_test)

training_score = classifier.score(X_train, y_train)
test_score = classifier.score(X_test, y_test)

print("Has a training accuracy of {} % ".format(round(training_score.mean(), 5) * 100))
print("Has a test accuracy of {} % ".format(round(test_score.mean(), 5) * 100))
# The accuracy score on its own is less useful for classification. Need to check the confusion matrix
# Notice how severe the overfitting is