machine-learning - Bootstrap aggregation (bagging) of logistic regression classifiers

Question

So I'm taking N bootstrap samples and training N logistic regression classifiers on these samples. Each classifier gives me some probability of being in a binary class and then I average these N probabilities to get a final prediction.

My question is if I took the N sets of regression coefficients and averaged those and used that averaged set of coefficients in a logistic regression classifier and took the output probability as the final prediction, is this the same as taking the average of the resultant N probabilities as described in the previous paragraph?

Answer 1

答案是否定的，因为逻辑函数是非线性的：1/(1+exp(-a)) + 1/(1+exp(-b))不等于1/(1+exp(-(a+b)))。

但是逻辑函数的倒数（也称为对数赔率）是线性的（g(x)在这个 wiki 页面中）。如果您正在计算对数赔率，您可以在 bagging 过程中平均相应的系数（beta0和页面中的）。beta1