/Python/From sklearn.metrics import classification_report

From sklearn.metrics import classification_report

Classification report scikit

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

>>> 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

Source: scikit-learn.org

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