Python LogisticRegression.predict_proba 的 scikit-learn 返回值
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scikit-learn return value of LogisticRegression.predict_proba
提问by Zelphir Kaltstahl
What exactly does the LogisticRegression.predict_probafunction return?
LogisticRegression.predict_proba函数究竟返回什么？
In my example I get a result like this:
在我的示例中，我得到如下结果：
[[ 4.65761066e-03 9.95342389e-01]
[ 9.75851270e-01 2.41487300e-02]
[ 9.99983374e-01 1.66258341e-05]]
From other calculations, using the sigmoid function, I know, that the second column are probabilities. The documentationsays, that the first column are n_samples, but that can't be, because my samples are reviews, which are texts and not numbers. The documentation also says, that the second column are n_classes. That certainly can't be, since I only have two classes (namely +1and -1) and the function is supposed to be about calculating probabilities of samples really being of a class, but not the classes themselves.
从其他计算中，使用 sigmoid 函数，我知道第二列是概率。该文件说，第一列是n_samples，但那是不可能的，因为我的样品的评价，这是文字和数字没有。文档还说，第二列是n_classes. 那当然不可能，因为我只有两个类（即+1和-1）并且该函数应该是关于计算样本真正属于一个类的概率，而不是类本身。
What is the first column really and why it is there?
真正的第一列是什么，为什么会出现在那里？
回答by iulian
4.65761066e-03 + 9.95342389e-01 = 1
9.75851270e-01 + 2.41487300e-02 = 1
9.99983374e-01 + 1.66258341e-05 = 1
The first column is the probability that the entry has the -1label and the second column is the probability that the entry has the +1label.
第一列是条目具有-1标签的概率，第二列是条目具有+1标签的概率。
If you would like to get the predicted probabilities for the positive label only, you can use logistic_model.predict_proba(data)[:,1]. This will yield you the [9.95342389e-01, 2.41487300e-02, 1.66258341e-05]result.
如果您只想获得正标签的预测概率，您可以使用logistic_model.predict_proba(data)[:,1]. 这将为您带来[9.95342389e-01, 2.41487300e-02, 1.66258341e-05]结果。
