8.19.1.12. 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

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

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.

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] :

f1_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

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

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)