从scikit-学习逻辑回归模型计算残余偏差 [英] Calculate residual deviance from scikit-learn logistic regression model

问题描述

有什么方法可以计算 scikit的残余偏差学习逻辑回归模型?这是R模型摘要的标准输出，但我在sklearn的任何文档中都找不到.

Is there any way to calculate residual deviance of a scikit-learn logistic regression model? This is a standard output from R model summaries, but I couldn't find it any of sklearn's documentation.

推荐答案

1. 如@ russell-richie所建议，应为model.predict_proba
2. 不要忘记函数metrics.log_loss()中的参数normalize=False返回每个样本损失的总和.

1. As suggested by @russell-richie, it should be model.predict_proba
2. Don't forget the argument normalize=False in function metrics.log_loss() to return the sum of the per-sample losses.

因此，要完成@ingo的答案，并获得sklearn.linear_model.LogisticRegression的模型偏差，可以计算:

So to complete @ingo's answer, to obtain the model deviance with sklearn.linear_model.LogisticRegression, you can compute:

def deviance(X, y, model):
    return 2*metrics.log_loss(y, model.predict_proba(X), normalize=False)

这篇关于从scikit-学习逻辑回归模型计算残余偏差的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！