GAM

• Early explaining systems for ML black boxes go back to 1986 with Generalized Additive Models (GAM)
• GAMs are global statistic models that use smooth functions, which are estimated using a scatterplot smoother
• The technique is applicable to any likelihood-based regression model, provides a flexible method for identifying nonlinear covariate effects in exponential family models and other likelihood-based regression models, and has the advantage of being completely automatic
• In its most general form, the algorithm can be applied to any situation in which a criterion is optimized involving one or more smooth functions