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wsrf  

Weighted Subspace Random Forest for Classification
View on CRAN: Click here


Download and install wsrf package within the R console
Install from CRAN:
install.packages("wsrf")

Install from Github:
library("remotes")
install_github("cran/wsrf")

Install by package version:
library("remotes")
install_version("wsrf", "1.7.30")



Attach the package and use:
library("wsrf")
Maintained by
He Zhao
[Scholar Profile | Author Map]
First Published: 2014-01-08
Latest Update: 2023-01-06
Description:
A parallel implementation of Weighted Subspace Random Forest.The Weighted Subspace Random Forest algorithm was proposed in the International Journal of Data Warehousing and Mining by Baoxun Xu, Joshua Zhexue Huang, Graham Williams, Qiang Wang, and Yunming Ye (2012) <doi:10.4018/jdwm.2012040103>.The algorithm can classify very high-dimensional data with random forests built using small subspaces.A novel variable weighting method is used for variable subspace selection in place of the traditional random variable sampling.This new approach is particularly useful in building models from high-dimensional data.
How to cite:
He Zhao (2014). wsrf: Weighted Subspace Random Forest for Classification. R package version 1.7.30, https://cran.r-project.org/web/packages/wsrf. Accessed 05 May. 2025.
Previous versions and publish date:
1.2.523 (2014-01-08 10:32), 1.3.0 (2014-02-09 08:17), 1.4.0 (2014-05-30 09:07), 1.5.0 (2015-05-24 18:56), 1.5.14 (2015-06-09 08:25), 1.5.24 (2015-07-07 13:17), 1.5.29 (2015-10-10 15:43), 1.5.47 (2016-07-11 10:56), 1.6.25 (2016-09-10 18:11), 1.7.0 (2016-10-28 10:51), 1.7.10 (2017-03-28 08:05), 1.7.13 (2017-04-17 15:53), 1.7.17 (2017-09-25 10:47), 1.7.20 (2021-04-24 11:10), 1.7.21 (2021-04-28 14:30), 1.7.22 (2021-10-10 11:50), 1.7.27 (2022-03-13 14:30)
Other packages that cited wsrf R package
View wsrf citation profile
Other R packages that wsrf depends, imports, suggests or enhances
Complete documentation for wsrf
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