8.17.1.2. 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.
Note: 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
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. ])