sklearn.metrics.fbeta_score

sklearn.metrics.fbeta_score(y_true, y_pred, beta, labels=None, pos_label=1, average=’binary’, sample_weight=None)

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.

Parameters:

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' 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:

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

Sample weights.

Returns:

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.

See also

precision_recall_fscore_support, multilabel_confusion_matrix

Notes

When true positive + false positive == 0 or true positive + false negative == 0, f-score returns 0 and raises UndefinedMetricWarning.

References

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

[2]	Wikipedia entry for the F1-score

Examples

>>> 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)
... 
0.23...
>>> fbeta_score(y_true, y_pred, average='micro', beta=0.5)
... 
0.33...
>>> fbeta_score(y_true, y_pred, average='weighted', beta=0.5)
... 
0.23...
>>> fbeta_score(y_true, y_pred, average=None, beta=0.5)
... 
array([0.71..., 0.        , 0.        ])