mars.learn.metrics.fbeta_score(y_true, y_pred, *, beta, labels=None, pos_label=1, average='binary', sample_weight=None, zero_division='warn')[source]#

Compute the F-beta score

The F-beta score is the weighted harmonic mean of precision and recall, reaching its optimal value at 1 and its worst value at 0.

The beta parameter determines the weight of recall in the combined score. beta < 1 lends more weight to precision, while beta > 1 favors recall (beta -> 0 considers only precision, beta -> +inf only recall).

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.

  • beta (float) – Determines the weight of recall in the combined score.

  • 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.


fbeta_score – F-beta score of the positive class in binary classification or weighted average of the F-beta score of each class for the multiclass task.

Return type

float (if average is not None) or array of float, shape = [n_unique_labels]



R. Baeza-Yates and B. Ribeiro-Neto (2011). Modern Information Retrieval. Addison Wesley, pp. 327-328.


Wikipedia entry for the F1-score


>>> from mars.learn.metrics import fbeta_score
>>> y_true = [0, 1, 2, 0, 1, 2]
>>> y_pred = [0, 2, 1, 0, 0, 1]
>>> fbeta_score(y_true, y_pred, average='macro', beta=0.5)
>>> fbeta_score(y_true, y_pred, average='micro', beta=0.5)
>>> fbeta_score(y_true, y_pred, average='weighted', beta=0.5)
>>> fbeta_score(y_true, y_pred, average=None, beta=0.5)
array([0.71..., 0.        , 0.        ])


When true positive + false positive == 0 or true positive + false negative == 0, f-score returns 0 and raises UndefinedMetricWarning. This behavior can be modified with zero_division.