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 theUser 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.Whenoutput_dict isTrue, this will be ignored and thereturned 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 thefollowing 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 unweightedmean per label), weighted average (averaging the support-weighted meanper label), and sample average (only for multilabel classification).Micro average (averaging the total true positives, false negatives andfalse positives) is only shown for multi-label or multi-classwith a subset of classes, because it corresponds to accuracyotherwise and would be the same for all metrics.See alsoprecision_recall_fscore_support for more detailson averages.

Note that in binary classification, recall of the positive classis 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

>>>fromsklearn.metricsimportclassification_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         5weighted 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         3weighted avg       1.00      0.67      0.80         3