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GAMs are uniquely placed on the interpretability vs. precitive power continuum. In many applications they perform almost as well as more complex models, but are extremely interpretable.

A GAM is a statistical model in which the target variable depends on unknown smooth functions of the features, and interest focuses on inference about these smooth functions.

An exponential family distribution is specified for the target Y (.e.g Normal, Binomial or Poisson) along with a link function g (for example the identity or log functions) relating the expected value of Y to the predictor variables.

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