CAST (version 1.0.0)

bss: Best subset feature selection

Description

Evaluate all combinations of predictors during model training

Usage

bss(
  predictors,
  response,
  method = "rf",
  metric = ifelse(is.factor(response), "Accuracy", "RMSE"),
  maximize = ifelse(metric == "RMSE", FALSE, TRUE),
  globalval = FALSE,
  trControl = caret::trainControl(),
  tuneLength = 3,
  tuneGrid = NULL,
  seed = 100,
  verbose = TRUE,
  ...
)

Value

A list of class train. Beside of the usual train content the object contains the vector "selectedvars" and "selectedvars_perf" that give the best variables selected as well as their corresponding performance. It also contains "perf_all" that gives the performance of all model runs.

Arguments

predictors

see train

response

see train

method

see train

metric

see train

maximize

see train

globalval

Logical. Should models be evaluated based on 'global' performance? See global_validation

trControl

see train

tuneLength

see train

tuneGrid

see train

seed

A random number

verbose

Logical. Should information about the progress be printed?

...

arguments passed to the classification or regression routine (such as randomForest).

Author

Hanna Meyer

Details

bss is an alternative to ffs and ideal if the training set is small. Models are iteratively fitted using all different combinations of predictor variables. Hence, 2^X models are calculated. Don't try running bss on very large datasets because the computation time is much higher compared to ffs.

The internal cross validation can be run in parallel. See information on parallel processing of carets train functions for details.

See Also

train,ffs, trainControl,CreateSpacetimeFolds, nndm

Examples

if (FALSE) {
data(iris)
bssmodel <- bss(iris[,1:4],iris$Species)
bssmodel$perf_all
plot(bssmodel)
}