sklearn.metrics.precision_score

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

Compute the precision

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 best value is 1 and the worst value is 0.

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.

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 :

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

Precision of the positive class in binary classification or weighted average of the precision of each class for the multiclass task.

Examples

>>> from sklearn.metrics import precision_score
>>> y_true = [0, 1, 2, 0, 1, 2]
>>> y_pred = [0, 2, 1, 0, 0, 1]
>>> precision_score(y_true, y_pred, average='macro')  
0.22...
>>> precision_score(y_true, y_pred, average='micro')  
0.33...
>>> precision_score(y_true, y_pred, average='weighted')
... 
0.22...
>>> precision_score(y_true, y_pred, average=None)  
array([ 0.66...,  0.        ,  0.        ])