feature_importance: Compute feature importance

In hrvg/regionaldrivers: Statistical analysis of regional drivers for classifications

Description

Compute feature importance

Usage

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

hrvg/regionaldrivers documentation built on June 20, 2021, 7:50 a.m.