Other packages > Find by keyword >

StratifiedRF  

Builds Trees by Sampling Variables in Groups
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]
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 05 Aug. 2026.
Previous versions and publish date:
(2026-07-09 08:26), 0.1.1 (2017-06-21 17:25)
Other packages that cited StratifiedRF R package
View StratifiedRF citation profile
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
Downloads during the last 30 days

Today's Hot Picks in Authors and Packages

noisyr  
Noise Quantification in High Throughput Sequencing Output
Quantifies and removes technical noise from high-throughput sequencing data. Two approaches are use ...
Download / Learn more Package Citations See dependency  
dhReg  
Dynamic Harmonic Regression
Building and forecasting time series data with multiple seasonality using Dynamic Harmonic Regressio ...
Download / Learn more Package Citations See dependency  
dcov  
A Fast Implementation of Distance Covariance
Efficient methods for computing distance covariance and relevant statistics. See Sz ...
Download / Learn more Package Citations See dependency  
kernelPSI  
Post-Selection Inference for Nonlinear Variable Selection
Different post-selection inference strategies for kernelselection as described in kernelPSI a Post-S ...
Download / Learn more Package Citations See dependency  
quickcode  
Quick and Essential 'R' Tricks for Better Scripts
The NOT functions, 'R' tricks and a compilation of some simple quick plus often used 'R' codes to im ...
Download / Learn more Package Citations See dependency  

28,083

R Packages

239,283

Dependencies

74,457

Author Associations

28,084

Publication Badges

© Copyright since 2022. All right reserved, rpkg.net.  Based in Cambridge, Massachusetts, USA