9.8.1.4. sklearn.metrics.precision_score¶
- sklearn.metrics.precision_score(y_true, y_pred, pos_label=1)¶
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, shape = [n_samples]
true targets
y_pred : array, shape = [n_samples]
predicted targets
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. Not used in the case of multiclass classification.
Returns : precision : float
precision of the positive class in binary classification or weighted avergage of the precision of each class for the multiclass task