sklearn.metrics.classification_report

sklearn.metrics.classification_report(y_true, y_pred, *, labels=None, target_names=None, sample_weight=None, digits=2, output_dict=False, zero_division='warn')[source]

Build a text report showing the main classification metrics.

Read more in the User Guide.

Parameters
y_true1d array-like, or label indicator array / sparse matrix

Ground truth (correct) target values.

y_pred1d array-like, or label indicator array / sparse matrix

Estimated targets as returned by a classifier.

labelsarray-like of shape (n_labels,), default=None

Optional list of label indices to include in the report.

target_nameslist of str of shape (n_labels,), default=None

Optional display names matching the labels (same order).

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

digitsint, default=2

Number of digits for formatting output floating point values. When output_dict is True, this will be ignored and the returned values will not be rounded.

output_dictbool, default=False

If True, return output as dict.

New in version 0.20.

zero_division“warn”, 0 or 1, default=”warn”

Sets the value to return when there is a zero division. If set to “warn”, this acts as 0, but warnings are also raised.

Returns
reportstr or dict

Text summary of the precision, recall, F1 score for each class. Dictionary returned if output_dict is True. Dictionary has the following structure:

{'label 1': {'precision':0.5,
             'recall':1.0,
             'f1-score':0.67,
             'support':1},
 'label 2': { ... },
  ...
}

The reported averages include macro average (averaging the unweighted mean per label), weighted average (averaging the support-weighted mean per label), and sample average (only for multilabel classification). Micro average (averaging the total true positives, false negatives and false positives) is only shown for multi-label or multi-class with a subset of classes, because it corresponds to accuracy otherwise and would be the same for all metrics. See also precision_recall_fscore_support for more details on averages.

Note that in binary classification, recall of the positive class is also known as “sensitivity”; recall of the negative class is “specificity”.

See also

precision_recall_fscore_support

Compute precision, recall, F-measure and support for each class.

confusion_matrix

Compute confusion matrix to evaluate the accuracy of a classification.

multilabel_confusion_matrix

Compute a confusion matrix for each class or sample.

Examples

>>> 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

    accuracy                           0.60         5
   macro avg       0.50      0.56      0.49         5
weighted avg       0.70      0.60      0.61         5

>>> y_pred = [1, 1, 0]
>>> y_true = [1, 1, 1]
>>> print(classification_report(y_true, y_pred, labels=[1, 2, 3]))
              precision    recall  f1-score   support

           1       1.00      0.67      0.80         3
           2       0.00      0.00      0.00         0
           3       0.00      0.00      0.00         0

   micro avg       1.00      0.67      0.80         3
   macro avg       0.33      0.22      0.27         3
weighted avg       1.00      0.67      0.80         3

Examples using sklearn.metrics.classification_report