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Description

Binomial Random Forest Feature Selection.

The 'binomialRF' is a new feature selection technique for decision trees that aims at providing an alternative approach to identify significant feature subsets using binomial distributional assumptions (Rachid Zaim, S., et al. (2019)) <doi:10.1101/681973>. Treating each splitting variable selection as a set of exchangeable correlated Bernoulli trials, 'binomialRF' then tests whether a feature is selected more often than by random chance.

binomialRF R Package

The binomialRF package is an R package that provides a feature selection algorithm to be used in randomForest classifiers. Treating each tree as a quasi binomial stochastic process in a random forest, binomialRF determines a feature’s importance by how often they are selected in practice vs. as expected by random chance. Given that trees are co-dependent as they subsample the same data, a theoretical adjustment is made using a generalization of the binomial distribution that adds a parameter to model correlation/association between trials.

Installing from CRAN

The binomialRF R package is on CRAN, and you can install as follows:

install.packages('binomialRF')

The CRAN version will always be the most stable release.

Installing from GitHub

To install experimental updates from the binomialRF , install it from GitHub directly, follow the code instructions below!

install.packages("devtools")


# The following dependencies might need to be installed
# manually if they're not installed by devtools. 

install.packages(c("ggplot2", "randomForest", "data.table","rlist", "correlbinom"))
devtools::install_github("SamirRachidZaim/binomialRF")
library(binomialRF)

These GitHub updates and features are experimental and will not be available in the CRAN version until the next, stable release is pushed.

References:

The main manuscript is included as a preprint in bioRxiv: https://doi.org/10.1101/681973, and has also been submitted for consideration at Frontiers in Genetics.

Metadata

Version

0.1.0

License

Unknown

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