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: |
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Returns: |
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See also
roc_auc_score
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.
[1] | Wikipedia entry for the Receiver operating characteristic |
[2] | Fawcett T. An introduction to ROC analysis[J]. Pattern Recognition Letters, 2006, 27(8):861-874. |
>>> 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 ])
sklearn.metrics.roc_curve
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Licensed under the 3-clause BSD License.
http://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html