Package: interpret
Title: Fit Interpretable Machine Learning Models and Explain Blackbox
        Machine Learning
Version: 0.1.24
Date: 2019-12-10
Description: Package for training interpretable machine learning models and explaining blackbox systems. 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 interpretability. 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("Microsoft Corporation", role="cph")
   )
Depends: R (>= 3.0.0)
NeedsCompilation: yes
SystemRequirements: C++11
Packaged: 2019-12-12 06:56:51 UTC; admins
Author: Samuel Jenkins [aut],
  Harsha Nori [aut],
  Paul Koch [aut],
  Rich Caruana [aut, cre],
  Microsoft Corporation [cph]
Maintainer: Rich Caruana <interpretml@outlook.com>
Repository: CRAN
Date/Publication: 2019-12-12 09:10:08 UTC
Built: R 4.0.0; x86_64-w64-mingw32; 2020-03-12 06:16:12 UTC; windows
Archs: i386, x64
