nomewang
commented
9 months ago Instead of limiting the score to lie between 0 and 1, the AUC metric is calculated by constantly looking for thresholds for True Positive Rate and True Positive Rate. Here is the description of sklearn: In the binary case, you can either provide the probability estimates, using the classifier.predict_proba() method, or the non-thresholded decision values given by the classifier.decisionfunction() method. In the case of providing the probability estimates, the probability of the class with the “greater label” should be provided. The “greater label” corresponds to classifier.classes[1] and thus classifier.predict_proba(X)[:, 1]. Therefore, the y_score parameter is of size (n_samples,).
limaodaxia
为什么计算ROC-AUC值的时候，真实的label为0或1，预测的分数是10~30左右的数值，这样也能计算ROC-AUC值吗？常规来说，预测的值应该在0到1之间，这样才能划分阈值，然后画出ROC曲线，计算出AUC值吧。