rfeControl {caret}R Documentation

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 == "cv", 10, 25),
           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
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''
p 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 parallel. See the examples in rfe.
computeArgs Extra arguments to pass into the ... slore in computeFunction. See the examples in rfe.

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 function should return a model object that can be used to generate predictions.

The pred function returns a vector of predictions (numeric or factors) from the current model. The arguments are:

The rank function is used to return the predictors in the order of the most important to the least important. Inputs are:

The function should return a data frame with a column called vars that has the current variable names. The first row should be the most important predictor etc. Other columns can be included in the output and will be returned in the final rfe object.

The selectSize function determines the optimal number of predictors based on the resampling output. Inputs for the function are:

This function should return an integer corresponding to the optimal subset size. caret comes with two examples functions for this purpose: pickSizeBest and pickSizeTolerance.

After the optimal subset size is determined, the selectVar function will be used to calculate the best rankings for each variable across all the resampling iterations. Inputs for the function are:

This function should return a character string of predictor names (of length size) in the order of most important to least important

Examples of these functions are included in the package: lmFuncs, rfFuncs, treebagFuncs and nbFuncs.

Model details about these functions, including examples, are in the package vignette for feature selection.

Value

A list

Author(s)

Max Kuhn

See Also

rfe, lmFuncs, rfFuncs, treebagFuncs, nbFuncs, pickSizeBest, pickSizeTolerance


[Package caret version 4.31 Index]