rfVarSelect: Variable Selection by Random Forest

In egenn/rtemis: Machine Learning and Visualization

Variable Selection by Random Forest

Description

Select important variables from a set of features based on RF-estimated variable importance

Usage

rfVarSelect(x, y, p = 0.2, print.plot = TRUE, verbose = TRUE)

Arguments

x	Predictors
y	outcome
p	Float (0, 1): Fraction of variables in x to select. p * ncol(x). May help to set to a fraction twice what you expect to be the true fraction of useful variables, to reduce false negatives at the expense of false positives which can be dealt by an appropriate learning algorithm.
print.plot	Logical: If TRUE, print plot of variable importance
verbose	Logical: If TRUE, print messages to console.

Details

Please note that this function is included for academic and exploratory purposes. It may be best to rely on each supervised learning algorithm's own variable selection approach.

Author(s)

E.D. Gennatas

egenn/rtemis documentation built on Dec. 17, 2024, 6:16 p.m.