8.16.1.2. sklearn.metrics.roc_curve¶
- sklearn.metrics.roc_curve(y_true, y_score)¶
compute Receiver operating characteristic (ROC)
Note: this implementation is restricted to the binary classification task.
Parameters : y_true : array, shape = [n_samples]
true binary labels
y_score : array, shape = [n_samples]
target scores, can either be probability estimates of the positive class, confidence values, or binary decisions.
Returns : fpr : array, shape = [>2]
False Positive Rates
tpr : array, shape = [>2]
True Positive Rates
thresholds : array, shape = [>2]
Thresholds on y_score used to compute fpr and tpr
Notes
References: http://en.wikipedia.org/wiki/Receiver_operating_characteristic
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) >>> fpr array([ 0. , 0.5, 0.5, 1. ])