mars.learn.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.

  • 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 (list, optional) – 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.

  • pos_label (str or int, 1 by default) – 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 (string, [None, 'binary' (default), 'micro', 'macro', 'samples', 'weighted']) –

    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:


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


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


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


    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.


    Calculate metrics for each instance, and find their average (only meaningful for multilabel classification where this differs from accuracy_score()).

  • sample_weight (array-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.


f1_score – F1 score of the positive class in binary classification or weighted average of the F1 scores of each class for the multiclass task.

Return type

float or array of float, shape = [n_unique_labels]



Wikipedia entry for the F1-score


>>> from mars.learn.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')
>>> f1_score(y_true, y_pred, average='micro')
>>> f1_score(y_true, y_pred, average='weighted')
>>> 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)


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.