8.16.1.10. 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 y_score used to compute precision and recall