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: projecteuclid.org/journals/st...

[@pectenmaximus231]

Beautiful presentation. So clear and informative!

[@mohamedrefaat197]

Very clear and concise! Looking forward for the follow up video

[@DelhiiteChetan]

So much information compiled together wonderfully in this video.Hope you come up with more such videos on individual methods with some examples.