# Changing logistic regression's loss function [closed]

We're using logistic regression to predict events probability. Logistic regression tries to minimize the residual variance (sum of squared residuals). However, in our specific problem we would like to use a different loss function (for the errors in the logistic regression). Does anyone knows any implementation (or how to implement) such change? (preferably in R or Python) Thanks!

• The typical approach to fitting logistic regression models is via maximum likelihood. This is not equivalent to minimizing the variance of the residuals. Dec 28, 2011 at 14:42
• What kind of loss function are you interested in? You can always use general-purpose nonlinear minimization functions, but often there are better application-specific approaches. Dec 28, 2011 at 14:45
• But is there any other method to fit a logit function to a 0s & 1s response using a different loss function? Dec 28, 2011 at 14:47
• there are several different functions. I get the "score" for the probabilities estimation according to these functions, so I want, the fitted function to be estimated according to those "scores" (and not just by using maximum likelihood Dec 28, 2011 at 14:49
• @Adam: The logistic regression model is logically separate from the means by which one fits it. The model simply says that each observation is distributed as a Bernoulli independently of the other observations and with a probability that is a particular function of additional covariates. Choosing to fit such a model by maximum likelihood is an entirely separate matter. Choosing to implement a maximum-likelihood fit by IRLS is yet another, separate, matter. Dec 28, 2011 at 23:13