sklearn.metrics.fbeta_score

sklearn.metrics.fbeta_score(y_true, y_pred, beta, labels=None, pos_label=1, average='weighted')

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).

Parameters :

y_true : array-like or list of labels or label indicator matrix

Ground truth (correct) target values.

y_pred : array-like or list of labels or label indicator matrix

Estimated targets as returned by a classifier.

beta: float :

Weight of precision in harmonic mean.

labels : array

Integer array of labels.

pos_label : str or int, 1 by default

If average is not None and the classification target is binary, only this class’s scores will be returned.

average : string, [None, ‘micro’, ‘macro’, ‘samples’, ‘weighted’ (default)]

If None, the scores for each class are returned. Otherwise, unless pos_label is given in binary classification, this determines the type of averaging performed on the data:

'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).

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.

References

[R159]

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

[R160]

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.        ])