Generalized Additive Models

In statistics, the generalized additive model (or GAM) is a statistical model developed by Trevor Hastie and Rob Tibshirani for blending properties of generalized linear models) with additive models.

The model specifies a distribution (such as a normal distribution, or a binomial distribution) and a link function g relating the expected value of the distribution to the predictors, and attempts to fit functions fi(xi) to satisfy:

The functions fi(xi) may be fit using parametric or non-parametric means, thus providing the potential for better fits to data than other methods. The method hence is very general - a typical GAM might use a scatterplot smoothing function such as a locally weighted mean for f1(x1), and then use a factor model for f2(x2). By allowing nonparametric fits, well designed GAMs allow good fits to the training data with relaxed assumptions on the actual relationship, perhaps at the expense of interpretability of results.

Overfitting