8.19.2.14. sklearn.metrics.precision_recall_fscore_support

sklearn.metrics.precision_recall_fscore_support(y_true, y_pred, beta=1.0, labels=None, pos_label=1, average=None)

Compute precision, recall, F-measure and support for each class

The precision is the ratio tp / (tp + fp) where tp is the number of true positives and fp the number of false positives. The precision is intuitively the ability of the classifier not to label as positive a sample that is negative.

The recall is the ratio tp / (tp + fn) where tp is the number of true positives and fn the number of false negatives. The recall is intuitively the ability of the classifier to find all the positive samples.

The F-beta score can be interpreted as a weighted harmonic mean of the precision and recall, where an F-beta score reaches its best value at 1 and worst score at 0.

The F-beta score weights recall more than precision by a factor of beta. beta == 1.0 means recall and precsion are equally important.

The support is the number of occurrences of each class in y_true.

If pos_label is None, this function returns the average precision, recall and F-measure if average is one of 'micro', 'macro', 'weighted'.

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, 1.0 by default

The strength of recall versus precision in the F-score.

labels : array

Integer array of labels.

pos_label : 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 (default), ‘micro’, ‘macro’, ‘weighted’]

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:

'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.

Returns :

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

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

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

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

References

[R135]

Wikipedia entry for the Precision and recall

[R136]

Wikipedia entry for the F1-score

Examples

In the binary case:

>>> from sklearn.metrics import precision_recall_fscore_support
>>> y_pred = [0, 1, 0, 0]
>>> y_true = [0, 1, 0, 1]
>>> p, r, f, s = precision_recall_fscore_support(y_true, y_pred, beta=0.5)
>>> p  
array([ 0.66...,  1.        ])
>>> r
array([ 1. ,  0.5])
>>> f  
array([ 0.71...,  0.83...])
>>> s  
array([2, 2]...)

In the multiclass case:

>>> from sklearn.metrics import precision_recall_fscore_support
>>> y_true = np.array([0, 1, 2, 0, 1, 2])
>>> y_pred = np.array([0, 2, 1, 0, 0, 1])
>>> precision_recall_fscore_support(y_true, y_pred, average='macro')        
(0.22..., 0.33..., 0.26..., None)
>>> precision_recall_fscore_support(y_true, y_pred, average='micro')        
(0.33..., 0.33..., 0.33..., None)
>>> precision_recall_fscore_support(y_true, y_pred, average='weighted')        
(0.22..., 0.33..., 0.26..., None)