rfeControl: Controlling the Feature Selection Algorithms

Description

This function generates a control object that can be used to specify the details of the feature selection algorithms used in this package.

Usage

rfeControl(functions = NULL,
           rerank = FALSE,
           method = "boot",
           saveDetails = FALSE,
           number = ifelse(method %in% c("cv", "repeatedcv"), 10, 25),
           repeats = ifelse(method %in% c("cv", "repeatedcv"), 1, number),
           verbose = FALSE,
           returnResamp = "all",
           p = .75,
           index = NULL,
           timingSamps = 0)

Arguments

Value

A list

code

nbFuncs

itemize

item

x
x
y
metric
maximize
size

pkg

caret

Details

Backwards selection requires function to be specified for some operations.

The fit function builds the model based on the current data set. The arguments for the function must be:

{ the current training set of predictor data with the appropriate subset of variables} y{ the current outcome data (either a numeric or factor vector)} first{ a single logical value for whether the current predictor set has all possible variables} last{ similar to first, but TRUE when the last model is fit with the final subset size and predictors.} ...{optional arguments to pass to the fit function in the call to rfe}