sklearn.metrics.f1_score(y_true, y_pred, *, labels=None, pos_label=1, average='binary', sample_weight=None, zero_division='warn') [source]
 
Compute the F1 score, also known as balanced F-score or F-measure. The F1 score can be interpreted as a weighted average of the precision and recall, where an F1 score reaches its best value at 1 and worst score at 0. The relative contribution of precision and recall to the F1 score are equal. The formula for the F1 score is: F1 = 2 * (precision * recall) / (precision + recall)
 In the multi-class and multi-label case, this is the average of the F1 score of each class with weighting depending on the average parameter. 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, default=None 

The set of labels to include when average != 'binary', and their order if average is None. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in y_true and y_pred are used in sorted order.  Changed in version 0.17: Parameter labels improved for multiclass problem.   
pos_labelstr or int, default=1 

The class to report if average='binary' and the data is binary. If the data are multiclass or multilabel, this will be ignored; setting labels=[pos_label] and average != 'binary' will report scores for that label only.  
average{‘micro’, ‘macro’, ‘samples’,’weighted’, ‘binary’} or None, default=’binary’ 

This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data:  
'binary': 

Only report results for the class specified by pos_label. This is applicable only if targets (y_{true,pred}) are binary.  
'micro': 

Calculate metrics globally by counting the total true positives, false negatives and false positives.  
'macro': 

Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.  
'weighted': 

Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters ‘macro’ to account for label imbalance; it can result in an F-score that is not between precision and recall.  
'samples': 

Calculate metrics for each instance, and find their average (only meaningful for multilabel classification where this differs from accuracy_score).    
sample_weightarray-like of shape (n_samples,), default=None 

Sample weights.  
zero_division“warn”, 0 or 1, default=”warn” 

Sets the value to return when there is a zero division, i.e. when all predictions and labels are negative. If set to “warn”, this acts as 0, but warnings are also raised.    Returns 
 
f1_scorefloat or array of float, shape = [n_unique_labels] 

F1 score of the positive class in binary classification or weighted average of the F1 scores of each class for the multiclass task.      See also  
fbeta_score, precision_recall_fscore_support, jaccard_score


multilabel_confusion_matrix

  Notes When true positive + false positive == 0, precision is undefined. When true positive + false negative == 0, recall is undefined. In such cases, by default the metric will be set to 0, as will f-score, and UndefinedMetricWarning will be raised. This behavior can be modified with zero_division. References  
1  
Wikipedia entry for the F1-score.   Examples >>> from sklearn.metrics import f1_score
>>> y_true = [0, 1, 2, 0, 1, 2]
>>> y_pred = [0, 2, 1, 0, 0, 1]
>>> f1_score(y_true, y_pred, average='macro')
0.26...
>>> f1_score(y_true, y_pred, average='micro')
0.33...
>>> f1_score(y_true, y_pred, average='weighted')
0.26...
>>> f1_score(y_true, y_pred, average=None)
array([0.8, 0. , 0. ])
>>> y_true = [0, 0, 0, 0, 0, 0]
>>> y_pred = [0, 0, 0, 0, 0, 0]
>>> f1_score(y_true, y_pred, zero_division=1)
1.0...