`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_namesarray-like 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”, 0.0, 1.0, np.nan}, 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.

New in version 1.3: np.nan option was added.

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`¶

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sklearn.metrics.classification_report¶

Examples using sklearn.metrics.classification_report¶

`sklearn.metrics`.classification_report¶

Examples using `sklearn.metrics.classification_report`¶