sklearn.metrics
.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_truendarray 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_scorendarray 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).
- pos_labelint or str, default=None
The label of the positive class. When
pos_label=None
, ify_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 some suboptimal thresholds which would not appear on a plotted ROC curve. This is useful in order to create lighter ROC curves.
New 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.
thresholds[0]
represents no instances being predicted and is arbitrarily set tomax(y_score) + 1
.
See also
plot_roc_curve
Plot Receiver operating characteristic (ROC) curve.
RocCurveDisplay
ROC Curve visualization.
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
andtpr
, which are sorted in reversed order during their calculation.References
- 1
- 2
Fawcett T. An introduction to ROC analysis[J]. Pattern Recognition Letters, 2006, 27(8):861-874.
Examples
>>> import numpy as np >>> from sklearn import 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([1.8 , 0.8 , 0.4 , 0.35, 0.1 ])