[darawk]

I think he makes a pretty good case for why they're wrong in the article. He also specifically addresses that question:

> I don’t know if it’s true that a plurality of people doing logistic regressions are using L2 regularization and lambda = 1, but the point is that it doesn’t matter. Unregularized logistic regression is the most obvious interpretation of a bare bones logistic regression, so it should be the default

> Why is this a problem? Because one might expect that the most basic version of a function should broadly work for most cases. Except that’s not actually what happens for LogisticRegression. Scikit-learn requires you to either preprocess your data or specify options that let you work with data that has not been preprocessed in this specific way.

Most people running an unparameterized logistic regression would expect that regression to work just fine on un-normalized data. However, a regularized LR is not going to work at all unless your data has been normalized. This is deeply counter-intuitive and has probably tripped thousands of people up who have no idea it even happened.