henckr/maidrr: Model-Agnostic Interpretable Data-driven suRRogate

The goal of maidrr is to aid you in the development of a Model-Agnostic Interpretable Data-driven suRRogate for your black box algorithm of choice. In short, these are the steps in the procedure: 1) Partial dependencies (PDs) are used to obtain model insights from the black box in the form of feature effects. 2) Those effects are used to group values/levels within a feature in an optimal data-driven way, while performing built-in feature selection. 3) An interpretable GLM surrogate is fit to the segmented features. Meaningful interactions can be included if desired.

install.packages("remotes")
remotes::install_github("henckr/maidrr")