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

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
Returns:	report : string Text summary of the precision, recall, F1 score for each class.

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