eliminate_features: Performs backwards variable selection using random forest...
In prescient/modelpipe: Fast and opinionated model development pipelines.

Performs backwards variable selection using random forest importance and leverages the ranger package.

eliminate_features(
  df_train,
  num_vars = 15,
  num_trees = 500,
  importance_type = "permutation",
  removal_rate = 1,
  verbose = T
)

`df_train`	Training data.frame with column called "target" for selection. All columns should be numeric and prepared with a package like vtreat or using a modelpipe prep_numeric or prep_bin function call. If you are working with categorical data you should convert "target" to a factor.
`num_vars`	Number of variables to retain.
`num_trees`	Number of trees to be used in Ranger.
`importance_type`	Specifies importance type. Valid values are one of 'none', 'impurity', 'impurity_corrected', 'permutation'. The default is 'permutation'.
`removal_rate`	Number of variables to remove at a time.
`verbose`	TRUE prints an update each time a variable is removed.