scikit-学习roc_auc_score()返回精度值 [英] scikit-learn roc_auc_score() returns accuracy values

问题描述

我正在尝试使用sklearn.metrics.roc_auc_score使用以下方法来计算ROC曲线下的面积:

I am trying to compute area under the ROC curve using sklearn.metrics.roc_auc_score using the following method:

roc_auc = sklearn.metrics.roc_auc_score(actual, predicted)

其中actual是带有地面真实分类标签的二进制向量，而predicted是带有我的分类器预测的分类标签的二进制向量.

where actual is a binary vector with ground truth classification labels and predicted is a binary vector with classification labels that my classifier has predicted.

但是，我得到的roc_auc的值与准确度值(正确预测了标签的样本比例)完全相似.这不是一次性的事情.我会在各种参数值上尝试分类器，并且每次获得相同结果时都会如此.

However, the value of roc_auc that I am getting is EXACTLY similar to accuracy values (proportion of samples whose labels are correctly predicted). This is not a one-off thing. I try my classifier on various values of the parameters and every time I get the same result.

我在做什么错了?

推荐答案

这是因为您正在传递分类器的决策，而不是其计算出的分数.最近在SO上对此问题有一个问题，并且对scikit-learn的相关拉取请求.

This is because you are passing in the decisions of you classifier instead of the scores it calculated. There was a question on this on SO recently and a related pull request to scikit-learn.

ROC曲线的点(及其下的区域)是您在分类阈值变化时研究精确调用折衷.默认情况下，在二进制分类任务中，如果分类器的得分为> 0.5，则预测为class1，否则为class0.更改该阈值时，您会得到类似 this 的曲线.曲线越高(曲线下方的面积越大)，分类器就越好.但是，要获得此曲线，您需要访问分类器的分数，而不是其决策.否则，无论决策阈值是多少，决策都将保持不变，并且AUC会退化为准确性.

The point of a ROC curve (and the area under it) is that you study the precision-recall tradeoff as the classification threshold is varied. By default in a binary classification task, if your classifier's score is > 0.5, then class1 is predicted, otherwise class0 is predicted. As you change that threshold, you get a curve like this. The higher up the curve is (more area under it), the better that classifier. However, to get this curve you need access to the scores of a classifier, not its decisions. Otherwise whatever the decision threshold is, the decision stay the same, and AUC degenerates to accuracy.

您正在使用哪个分类器?

Which classifier are you using?

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