Runaway Weights

You train logistic regression on a dataset where the two classes are perfectly linearly separable, with no regularization. What happens to the fitted coefficients as training continues?

They shrink toward zero since loss is already near minimal

Their magnitudes grow without bound as the model pushes predicted probabilities toward 0 and 1

They converge quickly to a unique finite solution

They oscillate forever around zero