8.19.1.8. 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 precision (beta == 0 considers only precision, beta == inf only recall).

Parameters:

y_true : array, shape = [n_samples]

Ground truth (correct) target values.

y_pred : array, shape = [n_samples]

Estimated targets as returned by a classifier.

beta: float :

Weight of precision in harmonic mean.

labels : array

Integer array of labels.

pos_label : int

In the binary classification case, give the label of the positive class (default is 1). Everything else but pos_label is considered to belong to the negative class. Set to None in the case of multiclass classification.

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

In the multiclass classification case, this determines the type of averaging performed on the data.

None:

Do not perform any averaging, return the scores for each class.

'macro':

Average over classes (does not take imbalance into account).

'micro':

Average over instances (takes imbalance into account). This implies that precision == recall == F1.

'weighted':

Average weighted by support (takes imbalance into account). Can result in F-score that is not between precision and recall. Do not perform any averaging, return the score for each class.

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

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

http://en.wikipedia.org/wiki/F1_score

Examples

In the binary case:

>>> from sklearn.metrics import fbeta_score
>>> y_pred = [0, 1, 0, 0]
>>> y_true = [0, 1, 0, 1]
>>> fbeta_score(y_true, y_pred, beta=0.5)  
0.83...
>>> fbeta_score(y_true, y_pred, beta=1)  
0.66...
>>> fbeta_score(y_true, y_pred, beta=2)  
0.55...

In the multiclass case:

>>> 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.        ])