The Generalized Linear Model is an extension of the linear model that allows for lots of different, non-linear models to be tested in the context of regression.
GLM is the mathematical framework used in many statistical analyses such as:
GLM is a supervised algorithm with a classic statistical technique (Supports thousands of input variables, text and transactional data) used for:
GLM implements:
Confidence bounds are supported with a
The General Linear model has two main characteristics:
That doesn't mean that the GLM can't handle non-additive or non-linear effects.
Removing the additive assumption:
GLM can accommodate such non-additive or non-linear effects with:
Methods that expand the scope of linear models and how they are fit: