Data Science, Machine Learning, Regression, Generalized Linear Model.
satisfied by the data. This makes GLMs a practical choice for many real world data sets that are nonlinear and heteroscedastic and in which we cannot assume that the model’s errors will always be normally distributed. Finally, a word of caution: Similar to Classical Linear Regression models, GLMs