sklearn.metrics.classification_report — scikit-learn 0.20.4 documentation (original) (raw)
sklearn.metrics. classification_report(y_true, y_pred, labels=None, target_names=None, sample_weight=None, digits=2, output_dict=False)[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. When output_dict is True, this will be ignored and the returned values will not be rounded. output_dict : bool (default = False) If True, return output as dict |
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| Returns: | report : string / 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 micro average (averaging the total true positives, false negatives and false positives), macro average (averaging the unweighted mean per label), weighted average (averaging the support-weighted mean per label) and sample average (only for multilabel classification). See alsoprecision_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 3micro avg 0.60 0.60 0.60 5 macro avg 0.50 0.56 0.49 5 weighted avg 0.70 0.60 0.61 5