There are good reasons other than `it models the data well' to use a logit. Of course, the perspective that much like least-squares, logit regression does best

There are good reasons other than `it models the data well' to use a logit. Of course, the perspective that much like least-squares, logit regression does best when your data is generated from a logistic GLM is true, but not the only story. In my opinion, the best justification for the logit is that it is a easily-optimizable member of the family of surrogate losses: http://fa.bianp.net/blog/2014/surrogate-loss-functions-in-machine-learning/ http://fa.bianp.net/blog/2014/surrogate-loss-functions-in-ma... The papers cited in the article pretty cleanly explain why the logit and other surrogate losses are the 'natural choice' for classification. This also explains why other non-generative models like the SVM perform similarly well to the logit in practice.