roc_curve#

sklearn.metrics.roc_curve(y_true, y_score, *, pos_label=None, sample_weight=None, drop_intermediate=True)[source] #

Compute Receiver operating characteristic (ROC).

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

Read more in the User Guide.

Parameters:
y_truearray-like of shape (n_samples,)

True binary labels. If labels are not either {-1, 1} or {0, 1}, then pos_label should be explicitly given.

y_scorearray-like of shape (n_samples,)

Target scores, can either be probability estimates of the positive class, confidence values, or non-thresholded measure of decisions (as returned by "decision_function" on some classifiers). For decision_function scores, values greater than or equal to zero should indicate the positive class.

pos_labelint, float, bool or str, default=None

The label of the positive class. When pos_label=None, if y_true is in {-1, 1} or {0, 1}, pos_label is set to 1, otherwise an error will be raised.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

drop_intermediatebool, default=True

Whether to drop thresholds where the resulting point is collinear with its neighbors in ROC space. This has no effect on the ROC AUC or visual shape of the curve, but reduces the number of plotted points.

Added in version 0.17: parameter drop_intermediate.

Returns:
fprndarray of shape (>2,)

Increasing false positive rates such that element i is the false positive rate of predictions with score >= thresholds[i].

tprndarray of shape (>2,)

Increasing true positive rates such that element i is the true positive rate of predictions with score >= thresholds[i].

thresholdsndarray of shape (n_thresholds,)

Decreasing thresholds on the decision function used to compute fpr and tpr. The first threshold is set to np.inf.

Changed in version 1.3: An arbitrary threshold at infinity (stored in thresholds[0]) is added to represent a classifier that always predicts the negative class, i.e. fpr=0 and tpr=0.

See also

RocCurveDisplay.from_estimator

Plot Receiver Operating Characteristic (ROC) curve given an estimator and some data.

RocCurveDisplay.from_predictions

Plot Receiver Operating Characteristic (ROC) curve given the true and predicted values.

det_curve

Compute error rates for different probability thresholds.

roc_auc_score

Compute the area under the ROC curve.

Notes

Since the thresholds are sorted from low to high values, they are reversed upon returning them to ensure they correspond to both fpr and tpr, which are sorted in reversed order during their calculation.

References

[2]

Fawcett T. An introduction to ROC analysis[J]. Pattern Recognition Letters, 2006, 27(8):861-874.

Examples

>>> importnumpyasnp
>>> fromsklearnimport metrics
>>> y = np.array([1, 1, 2, 2])
>>> scores = np.array([0.1, 0.4, 0.35, 0.8])
>>> fpr, tpr, thresholds = metrics.roc_curve(y, scores, pos_label=2)
>>> fpr
array([0. , 0. , 0.5, 0.5, 1. ])
>>> tpr
array([0. , 0.5, 0.5, 1. , 1. ])
>>> thresholds
array([ inf, 0.8 , 0.4 , 0.35, 0.1 ])

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