def evaluation(X_train, Y_train, X_test, Y_test, models):
    '''
    Runs test data through all models. Prints confusion matrices and classification reports.

    Parameters: training set and test set, array of models
    Returns: none
    '''
    for name, model in models:
            model.fit(X_train, Y_train)
            pred = model.predict(X_test)
            print('\n\n\n%s Accuracy: %.2f' % (name, accuracy_score(Y_test, pred)))
            labels = np.unique(Y_test)
            confusion = confusion_matrix(Y_test, pred, labels=labels)
            print('\nConfusion Matrix:')
            print(pd.DataFrame(confusion, index=labels, columns=labels))
            print('\nClassification Report:')
            print(classification_report(Y_test, pred))
    return