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xrf
View on CRAN: Click
here
Download and install xrf package within the R console
Install from CRAN:
install.packages("xrf")
Install from Github:
library("remotes")
install_github("cran/xrf")
Install by package version:
library("remotes")
install_version("xrf", "0.2.2")
Attach the package and use:
library("xrf")
Maintained by
Karl Holub
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-04-28
Latest Update: 2022-10-04
Description:
An implementation of the RuleFit algorithm as described in Friedman & Popescu (2008) <doi:10.1214/07-AOAS148>. eXtreme Gradient Boosting ('XGBoost') is used to build rules, and 'glmnet' is used to fit a sparse linear model on the raw and rule features. The result is a model that learns similarly to a tree ensemble, while often offering improved interpretability and achieving improved scoring runtime in live applications. Several algorithms for reducing rule complexity are provided, most notably hyperrectangle de-overlapping. All algorithms scale to several million rows and support sparse representations to handle tens of thousands of dimensions.
How to cite:
Karl Holub (2019). xrf: eXtreme RuleFit. R package version 0.2.2, https://cran.r-project.org/web/packages/xrf. Accessed 21 Nov. 2024.
Previous versions and publish date:
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imports, suggests or enhances
Complete documentation for xrf
Functions, R codes and Examples using
the xrf R package
Some associated functions: coef.xrf . model.matrix.xrf . predict.xrf . print.xrf . summary.xrf . xrf.formula . xrf .
Some associated R codes: glmnot.R . hyperrectangle_overlap.R . utils.R . xrf.R . Full xrf package functions and examples
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