sklearn.metrics.fbeta_score(y_true, y_pred, beta, labels=None, pos_label=1, average='binary', sample_weight=None)[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 precision 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 :

Weight of precision in harmonic mean.

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.

Changed in version 0.17: parameter labels improved for multiclass problem.

pos_label : str or int, 1 by default

The class to report if average='binary'. Until version 0.18 it is necessary to set pos_label=None if seeking to use another averaging method over binary targets.

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).

Note that if pos_label is given in binary classification with average != ‘binary’, only that positive class is reported. This behavior is deprecated and will change in version 0.18.

sample_weight : array-like of shape = [n_samples], optional

Sample weights.


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

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.


[R163]R. Baeza-Yates and B. Ribeiro-Neto (2011). Modern Information Retrieval. Addison Wesley, pp. 327-328.
[R164]Wikipedia entry for the F1-score


>>> from sklearn.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.        ])