Scikit-learn has separate functions for sklearn.linear_model.LinearRegression and sklearn.linear_model.Ridge. LinearRegression isn't regularized. From that, I'd

Scikit-learn has separate functions for sklearn.linear_model.LinearRegression and sklearn.linear_model.Ridge. LinearRegression isn't regularized. From that, I'd assume LogisticRegression isn't either. The sane paths ways to do it seem to be to make linear regression match logistic, or to make logistic match linear. It's not bad to have options, but the defaults should match what most users expect. It's an ML library, but people coming to ML from everywhere that has learned stats (most quantitative fields) are aligned around a different meaning of "logistic regression." Is it so crazy to default to that? When you tune the model, you're going to change the default anyways.