回忆在机器学习中意味着什么? [英] What does recall mean in Machine Learning?

问题描述

我知道搜索引擎中回忆的含义，但是分类器的回忆是什么意思，例如贝叶斯分类器?请举个例子，谢谢.

I know that the meaning of recall in search engine, but what's the meaning of recall of a classifier, e.g. bayes classifier? please give a an example, thanks.

例如，Precision =测试数据的正确/正确+错误的文档.如何理解召回?

for example, the Precision = correct/correct+wrong docs for test data. how to understand recall?

推荐答案

从字面上回忆是召回了多少 true 个阳性(发现) strong>，也就是找到了多少正确的匹配结果.

Recall literally is how many of the true positives were recalled (found), i.e. how many of the correct hits were also found.

精度(您的公式不正确)是有多少返回命中是 true 阳性，即有多少找到的是正确命中

Precision (your formula is incorrect) is how many of the returned hits were true positive i.e. how many of the found were correct hits.

实际上，这非常简单.

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