Control Object for Selection By Filtering (SBF)
Controls the execution of models with simple filters for feature selection
sbfControl(functions = NULL, 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 = 0.75, index = NULL, workers = 1, computeFunction = lapply, computeArgs = NULL)
- a list of functions for model fitting, prediction and variable filtering (see Details below)
- 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 ``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
Simple filter-based feature 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:
- a list that echos the specified arguments