classification_report (original) (raw)

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.

Added 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.

Added 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”.

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

Gallery examples#