print('Number of features = %d' % num_features)
    accuracy = sklearn.metrics.accuracy_score(labels, predicted_votes)
    precision = sklearn.metrics.precision_score(labels, predicted_votes)
    recall = sklearn.metrics.recall_score(labels, predicted_votes)
    print('Accuracy = %.3f, Precision = %.3f, Recall = %.3f' % (accuracy, precision, recall))
    print('Confusion matrix:')
    print(confusion_matrix(labels, predicted_votes, votes['class'].cat.categories))
    print('')