from sklearn.metrics import classification_report
target_names = ['first_value_y','second_value_y'] # target values
# Print classification report after a train/test split:
print(classification_report(y_test, y_pred, target_names=target_names))>>> from sklearn.metrics import classification_report
>>> y_true = [0, 1, 2, 2, 2]
>>> y_pred = [0, 0, 2, 2, 1]
>>> target_names = ['class 0', 'class 1', 'class 2']
>>> print(classification_report(y_true, y_pred, target_names=target_names))
precision recall f1-score support
class 0 0.50 1.00 0.67 1
class 1 0.00 0.00 0.00 1
class 2 1.00 0.67 0.80 3
avg / total 0.70 0.60 0.61 5