cran
. See also theR-universe documentation.Package: interpret 0.1.34
interpret: Fit Interpretable Machine Learning Models
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>).
Authors:
interpret_0.1.34.tar.gz
interpret_0.1.34.tar.gz(r-4.5-noble)interpret_0.1.34.tar.gz(r-4.4-noble)
interpret_0.1.34.tgz(r-4.4-emscripten)interpret_0.1.34.tgz(r-4.3-emscripten)
interpret.pdf |interpret.html✨
interpret/json (API)
# Install 'interpret' in R: |
install.packages('interpret', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/interpretml/interpret/issues
Last updated 29 days agofrom:8959d56801. Checks:OK: 2. Indexed: no.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 28 2024 |
R-4.5-linux-x86_64 | OK | Nov 28 2024 |
Exports:ebm_classifyebm_predict_probaebm_show
Dependencies:
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Build an EBM classification model | ebm_classify |
ebm_predict_proba | ebm_predict_proba |
ebm_show | ebm_show |