Logistic regression model is one of the models which often used in ML. Logistic regression can be phrased as latent variable models with error variables distributed as Gumbel distributions (type I generalized extreme value distributions). This phrasing is common in the theory of discrete choice models, which include logit models, probit models, and various extensions of them, and derives from the fact that the difference of two type-I GEV-distributed variables follows a logistic distribution, of which the logit function is the quantile function. The type-I GEV distribution thus plays the same role in these logit models as the normal distribution does in the corresponding probit models. GEV methods widely used in the treatment of tail risks and QQ-POT-QRM- extemeStat and more of these descriptions based on GEV.