9.8.1.10. sklearn.metrics.precision_recall_curve¶
- sklearn.metrics.precision_recall_curve(y_true, probas_pred)¶
Compute precision-recall pairs for different probability thresholds
Note: this implementation is restricted to the binary classification task.
The precision is the ratio
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
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.Parameters : y_true : array, shape = [n_samples]
true targets of binary classification in range {-1, 1} or {0, 1}
probas_pred : array, shape = [n_samples]
estimated probabilities
Returns : precision : array, shape = [n]
Precision values
recall : array, shape = [n]
Recall values
thresholds : array, shape = [n]
Thresholds on proba_ used to compute precision and recall