GLM vs. GAM - Generalized Additive Models

Additive and Generalized Additive models differ from LM/GLMs in the way they relate the mean to the x predictors. While G/LMs assume a linear model in x, G/AMs allow for any function approximation that captures the structure between mu (or g(mu)) and x. In this video we will also learn about the backfitting algorithm which is a general method for fitting G/AMs. In a future video we will talk about more efficient algorithms (P-IRLS) for specific function approximators called splines. Original GAM paper - Hastie and Tibshirani 1986: https://projecteuclid.org/journals/statistical-science/volume-1/issue-3/Generalized-Additive-Models/10.1214/ss/1177013604.full