二元向量作为 roc_curve 的 y_score 参数 [英] Binary vectors as y_score argument of roc_curve

问题描述

sklearn roc_curve docstring 指出:

The sklearn roc_curve docstring states:

"y_score : 数组，形状 = [n_samples]目标分数可以是正类的概率估计、置信度值或二元决策."

"y_score : array, shape = [n_samples] Target scores, can either be probability estimates of the positive class, confidence values, or binary decisions."

在什么情况下将 y_score 设置为二元向量(二元决策")是有意义的?难道这不会导致 ROC 曲线上有一个点，这与该点相悖吗?

In what situation it would make sense to set y_score to a binary vector ("binary decisions")? Wouldn't that result in a ROC curve with one point on it which kind of defies the point?

推荐答案

如果您使用的分类器不输出概率分数(例如 svm.SVC 没有明确的 probability=True)，没有办法计算 ROC 曲线.作为 API 设计者，您有两种选择:引发异常并且不向用户提供任何有用信息，或者用一个数据点绘制退化曲线.我认为后者更有用.

If you are using a classifier that does not output probability scores (e.g. svm.SVC without an explicit probability=True), there isn't a way to compute a ROC curve. As an API designer, you have two choices: raise an exception and provide the user no useful information, or plot a degenerate curve with one data point. I would argue the latter is more useful.

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