Generalized additive models (GAMs) are regressions that estimate nonlinear patterns in data. This tag should not be used with the `glm` tag unless the question explicitly deals with comparison of the GAMs with GLMs.
Generalized additive models (GAMs) essentially allow users to model curvilinear data in a manner more flexible than typical regression modeling. These regressions achieve this by fitting splines to data based on estimations from basis functions that approximate where the data is while penalizing overfitting common in techniques like LOESS. More information on these models can be found at this wiki page.
