Package: interpret
Title: Fit Interpretable Machine Learning Models
Version: 0.1.35
Date: 2026-02-25
Description: Package for training interpretable machine learning models. Historically, the most interpretable machine learning models were not very accurate, and the most accurate models were not very interpretable. Microsoft Research has developed an algorithm called the Explainable Boosting Machine (EBM) which has both high accuracy and interpretable characteristics. EBM uses machine learning techniques like bagging and boosting to breathe new life into traditional GAMs (Generalized Additive Models). This makes them as accurate as random forests and gradient boosted trees, and also enhances their intelligibility and editability. Details on the EBM algorithm can be found in the paper by Rich Caruana, Yin Lou, Johannes Gehrke, Paul Koch, Marc Sturm, and Noemie Elhadad (2015, <doi:10.1145/2783258.2788613>).
URL: https://github.com/interpretml/interpret
BugReports: https://github.com/interpretml/interpret/issues
License: MIT + file LICENSE
Authors@R: c(
   person("Samuel", "Jenkins", role = c("aut")),
   person("Harsha", "Nori", role = c("aut")),
   person("Paul", "Koch", role = c("aut")),
   person("Rich", "Caruana", role = c("aut", "cre"), email = "interpretml@outlook.com"),
   person("The InterpretML Contributors", role="cph")
   )
Depends: R (>= 3.0.0)
NeedsCompilation: yes
SystemRequirements: C++17
Packaged: 2026-02-26 00:25:32 UTC; runner
Author: Samuel Jenkins [aut],
  Harsha Nori [aut],
  Paul Koch [aut],
  Rich Caruana [aut, cre],
  The InterpretML Contributors [cph]
Maintainer: Rich Caruana <interpretml@outlook.com>
Repository: CRAN
Date/Publication: 2026-03-03 21:30:08 UTC
Built: R 4.6.0; x86_64-apple-darwin20; 2026-04-25 11:28:20 UTC; unix
Archs: interpret.so.dSYM
