Controlling the Feature Selection Algorithms
This function generates a control object that can be used to specify the details of the feature selection algorithms used in this package.
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)
- a list of functions for model fitting, prediction and variable importance (see Details below)
- a logical: should variable importance be re-calculated each time features are removed?
- The external resampling method:
LGOCV(for repeated training/test splits
- Either the number of folds or number of resampling iterations
- For repeated k-fold cross-validation only: the number of complete sets of folds to compute
- a logical to save the predictions and variable importances from the selection process
- a logical to print a log for each external resampling iteration
- 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
- a list with elements for each external resampling iteration. Each list element is the sample rows used for training at that iteration.
- an integer that specifies how many machines/processors will be used
- a function that is
lapply. It must have arguments
computeFunctioncan be used to build models in parall
- Extra arguments to pass into the
computeFunction. See the examples in
Backwards selection requires function to be specified for some operations.
fit function builds the model based on the current data set. The arguments for the function must be:
TRUEwhen the last model is fit with the final subset size and predictors.}
- A list