Compute feature importance

feature_importance(
  training_data,
  target_colname,
  filter_methods = c("FSelectorRcpp_information.gain"),
  .iters = 500,
  .first = 30,
  .split = 0.8,
  .stratify = TRUE,
  .seed = 1789
)

Arguments

training_data	a `data.frame`
target_colname	`character` the name of the column containing the target (output)
filter_methods	`list` of `character` accepted by `mlr::generateFilterValuesData()`
.iters	`numeric` number of iterations for the subsampling, default to 500
.first	`numeric`, number of feature to display, default to 30
.split	`numeric`, ratio of the subsampling splitting ratio, default to 0.8
.stratify	`logical`, should the subsampling be stratified, default to `TRUE`
.seed	`numeric`, fix seed for reproducible example

Value

a list with two elements a list of `data.frame` for each method and a list of `ggplot` objects