maidrr-package: maidrr: Model-Agnostic Interpretable Data-driven suRRogate

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

maidrr: Model-Agnostic Interpretable Data-driven suRRogate

Description

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.

henckr/maidrr documentation built on July 27, 2023, 3:17 p.m.