boruta_filter: Boruta filter

In nestedcv: Nested Cross-Validation with 'glmnet' and 'caret'

Boruta filter

Description

Filter using Boruta algorithm.

Usage

boruta_filter(
  y,
  x,
  select = c("Confirmed", "Tentative"),
  type = c("index", "names", "full"),
  ...
)

Arguments

y	Response vector
x	Matrix of predictors
select	Which type of features to retain. Options include "Confirmed" and/or "Tentative".
type	Type of vector returned. Default "index" returns indices, "names" returns predictor names, "full" returns a named vector of variable importance.
...	Other arguments passed to Boruta::Boruta()

Details

Boruta works differently from other filters in that it does not rank variables by variable importance, but tries to determine relevant features and divides features into Rejected, Tentative or Confirmed.

Value

Integer vector of indices of filtered parameters (type = "index") or character vector of names (type = "names") of filtered parameters. If type is "full" full output from Boruta is returned.

nestedcv documentation built on June 22, 2024, 11:30 a.m.