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interpret
View on CRAN: Click
here
Download and install interpret package within the R console
Install from CRAN:
install.packages("interpret")
Install from Github:
library("remotes")
install_github("cran/interpret")
Install by package version:
library("remotes")
install_version("interpret", "0.1.33")
Attach the package and use:
library("interpret")
Maintained by
Rich Caruana
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-10-06
Latest Update: 2023-01-27
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, ).
How to cite:
Rich Caruana (2019). interpret: Fit Interpretable Machine Learning Models. R package version 0.1.33, https://cran.r-project.org/web/packages/interpret
Previous versions and publish date:
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Other R packages that interpret depends,
imports, suggests or enhances
Functions, R codes and Examples using
the interpret R package
Some associated functions: ebm_classify . ebm_predict_proba . ebm_show .
Some associated R codes: binning.R . booster.R . dataset.R . ebm.R . interaction_detector.R . random_numbers.R . sampling.R . Full interpret package functions and examples
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