sbfControl

From caret v4.47 by Max Kuhn

Control Object for Selection By Filtering (SBF)

Controls the execution of models with simple filters for feature selection

Keywords: utilities

Usage

sbfControl(functions = NULL, 
           method = "boot", 
           saveDetails = FALSE, 
           number = ifelse(method == "cv", 10, 25), 
           verbose = TRUE, 
           returnResamp = "all", 
           p = 0.75, 
           index = NULL, 
           workers = 1, 
           computeFunction = lapply, 
           computeArgs = NULL)

Arguments

functions: a list of functions for model fitting, prediction and variable filtering (see Details below)
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 ``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 parall
computeArgs: Extra arguments to pass into the ... slore in computeFunction. See the examples in sbf.

Details

Simple filter-based feature 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 (i.e. after filtering)} y{ the current outcome data (either a numeric or factor vector)} ...{ optional arguments to pass to the fit function in the call to sbf}

Value

a list that echos the specified arguments

code

nbSBF

itemize

score

item

Community examples

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