sklearn.metrics.precision_recall_curve

sklearn.metrics.precision_recall_curve(y_true, probas_pred, pos_label=None, sample_weight=None)

Compute precision-recall pairs for different probability thresholds

Note: this implementation is restricted to the binary classification task.

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 last precision and recall values are 1. and 0. respectively and do not have a corresponding threshold. This ensures that the graph starts on the y axis.

Read more in the User Guide.

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 or decision function.

pos_label : int or str, default=None

The label of the positive class

sample_weight : array-like of shape = [n_samples], optional

Sample weights.

Returns:

precision : array, shape = [n_thresholds + 1]

Precision values such that element i is the precision of predictions with score >= thresholds[i] and the last element is 1.

recall : array, shape = [n_thresholds + 1]

Decreasing recall values such that element i is the recall of predictions with score >= thresholds[i] and the last element is 0.

thresholds : array, shape = [n_thresholds <= len(np.unique(probas_pred))]

Increasing thresholds on the decision function used to compute precision and recall.

See also

average_precision_score

Compute average precision from prediction scores

roc_curve

Compute Receiver operating characteristic (ROC) curve

Examples

>>> import numpy as np
>>> from sklearn.metrics import precision_recall_curve
>>> y_true = np.array([0, 0, 1, 1])
>>> y_scores = np.array([0.1, 0.4, 0.35, 0.8])
>>> precision, recall, thresholds = precision_recall_curve(
...     y_true, y_scores)
>>> precision  # doctest: +ELLIPSIS
array([0.66666667, 0.5       , 1.        , 1.        ])
>>> recall
array([1. , 0.5, 0.5, 0. ])
>>> thresholds
array([0.35, 0.4 , 0.8 ])

Examples using sklearn.metrics.precision_recall_curve

Precision-Recall