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StratifiedRF
View on CRAN: Click
here
Download and install StratifiedRF package within the R console
Install from CRAN:
install.packages("StratifiedRF")
Install from Github:
library("remotes")
install_github("cran/StratifiedRF")
Install by package version:
library("remotes")
install_version("StratifiedRF", "0.2.2")
Attach the package and use:
library("StratifiedRF")
Maintained by
David Cortes
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2017-06-21
Latest Update: 2017-06-30
Description:
Random Forest-like tree ensemble that works with groups of predictor variables. When building a tree, a number of variables is taken randomly from each group separately, thus ensuring that it considers variables from each group for the splits. Useful when rows contain information about different things (e.g. user information and product information) and it's not sensible to make a prediction with information from only one group of variables, or when there are far more variables from one group than the other and it's desired to have groups appear evenly on trees. Trees are grown using the C5.0 algorithm rather than the usual CART algorithm. Supports parallelization (multithreaded), missing values in predictors, and categorical variables (without doing One-Hot encoding in the processing). Can also be used to create a regular (non-stratified) Random Forest-like model, but made up of C5.0 trees and with some additional control options. As it's built with C5.0 trees, it works only for classification (not for regression).
How to cite:
David Cortes (2017). StratifiedRF: Builds Trees by Sampling Variables in Groups. R package version 0.2.2, https://cran.r-project.org/web/packages/StratifiedRF. Accessed 29 Jan. 2025.
Previous versions and publish date:
0.1.1 (2017-06-21 17:25)
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Other R packages that StratifiedRF depends,
imports, suggests or enhances
Complete documentation for StratifiedRF
Functions, R codes and Examples using
the StratifiedRF R package
Some associated functions: predict.stratified_rf . print.stratified_rf . stratified_rf . summary.stratified_rf . varimp_stratified_rf .
Some associated R codes: rf_c50.R . Full StratifiedRF package functions and examples
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