Best subset feature selection

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,
  ...
)

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).

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.

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.

Note

This variable selection is particularly suitable for spatial cross validations where variable selection MUST be based on the performance of the model for predicting new spatial units. Note that bss is very slow since all combinations of variables are tested. A more time efficient alternative is the forward feature selection (ffs).

Author

Hanna Meyer

Examples

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