# sklearn.metrics.classification_report¶

sklearn.metrics.classification_report(y_true, y_pred, labels=None, target_names=None, sample_weight=None, digits=2)[source]

Build a text report showing the main classification metrics

Read more in the User Guide.

Parameters: y_true : 1d array-like, or label indicator array / sparse matrix Ground truth (correct) target values. y_pred : 1d array-like, or label indicator array / sparse matrix Estimated targets as returned by a classifier. labels : array, shape = [n_labels] Optional list of label indices to include in the report. target_names : list of strings Optional display names matching the labels (same order). sample_weight : array-like of shape = [n_samples], optional Sample weights. digits : int Number of digits for formatting output floating point values report : string Text summary of the precision, recall, F1 score for each class. The reported averages are a prevalence-weighted macro-average across classes (equivalent to precision_recall_fscore_support with average='weighted'). 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

avg / total       0.70      0.60      0.61         5