Separation in a binary regression model occurs when an affine score assigns all observations from one class to one side of a threshold and all observations from the other class to the other side.
Complete separation means that every signed training margin is strictly positive for some affine score. In unpenalized logistic regression it causes coefficient norms to diverge and prevents a finite maximum-likelihood estimate.

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