select_mcfs: Feature Selection performed by MCFS (Monte Carlo Feature...

In ModelOriented/forester: Quick and Simple Tools for Training and Testing of Tree-Based Models

Feature Selection performed by MCFS (Monte Carlo Feature Selection) algorithm

Description

Unfortunately it is impossible to turn off the prints, as they come from used rmcfs package's back-end written in Java.

Usage

select_mcfs(
  data,
  y,
  cutoffPermutations = 20,
  threadsNumber = NULL,
  max_features = NULL,
  verbose = FALSE
)

Arguments

data	A data source, that is one of the major R formats: data.table, data.frame, matrix and so on.
y	A string that indicates a target column name.
cutoffPermutations	An non-negative integer value that determines the number of permutation runs. It needs at least 20 permutations for a statistically significant result. Minimum value of this parameter is 3, however if it is 0 then permutations method is turned off.
threadsNumber	A positive integer value describing the number of threads to use in computation. More threads needs more CPU cores as well as memory usage is a bit higher. It is recommended to set this value equal to or less than CPU available cores. By default set to NULL, which will use maximal number of cores minus 1.
max_features	A positive integer value describing the desired number of selected features. By default set to NULL, then the MCFS algorithm uses all features that seems important to it.
verbose	A logical value, if set to TRUE, provides all information about preprocessing process, if FALSE gives none.

Value

A list containing two objects:

• `data` A dataset with selected columns,

• `idx` The indexes of removed columns.

ModelOriented/forester documentation built on June 6, 2024, 7:29 a.m.