Additive models fit the expectation of a dependent variable as a sum of smooth functions, one for each dimension of the explanatory variables. Hastie and Tibshirani wrote a well known book about this subject. This way a GAM is reduced to a GLM with small modifications for the penalties.
Estimation is faster and regression diagnostics are easily computed.
In this presentation I show the basics of GAMs and apply them to real data sets. I also discuss the advantages for interpolation of irregularly spaced observations and for automatic extrapolation.