异常检测中的正类是什么? [英] What is the positive class in anomaly detection?

问题描述

目前，我尝试在 python 中使用 scikit-learn 实现异常检测算法.我将数据集重新标记为 Inliers(正常实例)标记为 1，而异常值(异常实例)标记为 -1(参考)

Currently, I try to implement an anomaly detection algorithm with scikit-learn in python. I relabeled the dataset to Inliers (Normal instances) are labelled 1, while outliers (Anomaly instances) are labelled -1 (Reference)

对于accuracy_score、precision_score、recall_score 和f1_score 的计算，当我设置pos_label=1 或pos_label=-1 时，我得到了不同的值.

For the calculation of accuracy_score, precision_score, recall_score and f1_score I get different values when I set pos_label=1 or pos_label=-1.

那么在异常检测的上下文中，正类的标签是什么:1还是-1?

So what is the label of the positive class in the context of anomaly detection: 1 or -1?

推荐答案

您有兴趣找出哪些样本是异常值.然后，正类是异常值.

注意:通常，您应该尝试提高召回率而不是准确率，因为您需要减少假阴性(预测异常值是内部值)

You are interested in finding what samples are outliers. Then, positive class is the outliers.

Note: Generally, you should try to improve the recall rather than the precision score, because you need to reduce false negatives (predict that an outlier is an inlier)

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