Explainable Boosting Machines

An interface to the 'Python' 'InterpretML' framework for fitting explainable boosting machines (EBMs); see Nori et al. (2019) for details. EBMs are a modern type of generalized additive model that use tree-based, cyclic gradient boosting with automatic interaction detection. They are often as accurate as state-of-the-art blackbox models while remaining completely interpretable.

ebm

A reticulate-powered interface to the Python InterpretML framework for fitting explainable boosting machines (EBMs). EBMs are a modern type of generalized additive model that use tree-based, cyclic gradient boosting with automatic interaction detection. They are often as accurate as state-of-the-art blackbox models while remaining completely interpretable.

Installation

Currently, you can only install the ebm package from GitHub (coming soon to CRAN):

# install.packages("remotes")
remotes::install_github("bgreenwell/ebm")

Usage

For a thorough overview of using the ebm package, see this article.