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 = TRUE,
           returnResamp = "all",
           p = .75,
           index = NULL,
           workers = 1,
           computeFunction = lapply,
           computeArgs = NULL)

Arguments

functions

a list of functions for model fitting, prediction and variable importance (see Details below)

rerank

a logical: should variable importance be re-calculated each time features are removed?

method

The external resampling method: boot, cv, LOOCV or LGOCV (for repeated training/test splits

number

Either the number of folds or number of resampling iterations

repeats

For repeated k-fold cross-validation only: the number of complete sets of folds to compute

saveDetails

a logical to save the predictions and variable importances from the selection process

verbose

a logical to print a log for each external resampling iteration

returnResamp

A character string indicating how much of the resampled summary metrics should be saved. Values can be ``final'', ``all'' or ``none''

For leave-group out cross-validation: the training percentage

index

a list with elements for each external resampling iteration. Each list element is the sample rows used for training at that iteration.

workers

an integer that specifies how many machines/processors will be used

computeFunction

a function that is lapply or emulates lapply. It must have arguments X, FUN and .... computeFunction can be used to build models in parall

computeArgs

Extra arguments to pass into the ... slore in computeFunction. See the examples in rfe.

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}