predicted_income = model.predict(income_feature_vecs[:num_rows, :])
    income_probabilities = model.predict_proba(income_feature_vecs[:num_rows, :])

    accuracy = sklearn.metrics.accuracy_score(income_labels[:num_rows], predicted_income)
    precision = sklearn.metrics.precision_score(income_labels[:num_rows], predicted_income)
    recall = sklearn.metrics.recall_score(income_labels[:num_rows], predicted_income)
    print('%6.0f rows:  Accuracy = %.3f, Precision = %.3f, Recall = %.3f' % (num_rows, accuracy, precision, recall))
    print('Confusion matrix:')
    print(confusion_matrix(
            income_labels[:num_rows], predicted_income, income.income.cat.categories), 'n')