[thanatropism]

Logistic regression essentially gives a conditional probability function, much like linear regression gives a conditional expectation function. You can compute log odds from logistic regression -- say, conditional to all other factors being left-handed makes you twice as likely to some binary effect. People were complaining that this isn't trivially done by staring at the coefficients, but people who can't think in partial derivatives shouldn't be in this business.

OTOH: if you assume an iid framework, the probabilistic marginal effects aren't even needed to go from something like "non-bottle blondes have probability p of being haired, bottle blondes have probability q" to "painting the hair of 1000 women will generate 1000*(q-p) jobs on average". Or you can parameterize a Poisson process for rare events and report exponential/Erlang waiting times. And so on.

[jonathankoren]

That's the problem. Log odds are not intuitive. Hell, even probabilities aren't intuitive, and that's much easier to think about. Look at all the people start crying that "it was wrong" when the less likely event happens when the prediction said 90% probability.

This isn't a crazy minority position "logistic regression is not interpretable" is truism from basic ML courses, and blog posts all over the internet.

[thanatropism]

It's odd that "ML theory" (really data science theory) as proposed by blog posts would supersede established statistics.

Something is rotten in the kingdom of Denmark.

[jonathankoren]

I think you’re just trolling now. More to the point, I think you fundamentally misunderstand what interpretability means.

Logistic regression as not being interpretable was drilled into me by one of the creators of AdaBoost in grad school. As I said, this is widely held position.