extract_inner_fselect_results: Extract Inner Feature Selection Results

In mlr3fselect: Feature Selection for 'mlr3'

Extract Inner Feature Selection Results

Description

Extract inner feature selection results of nested resampling. Implemented for mlr3::ResampleResult and mlr3::BenchmarkResult.

Usage

extract_inner_fselect_results(x, fselect_instance, ...)

Arguments

x	(mlr3::ResampleResult | mlr3::BenchmarkResult).
fselect_instance	(logical(1)) If TRUE, instances are added to the table.
...	(any) Additional arguments.

Details

The function iterates over the AutoFSelector objects and binds the feature selection results to a data.table::data.table(). AutoFSelector must be initialized with store_fselect_instance = TRUE and resample() or benchmark() must be called with store_models = TRUE. Optionally, the instance can be added for each iteration.

Value

data.table::data.table().

Data structure

The returned data table has the following columns:

• experiment (integer(1))
  Index, giving the according row number in the original benchmark grid.

• iteration (integer(1))
  Iteration of the outer resampling.

• One column for each feature of the task.

• One column for each performance measure.

• features (character())
  Vector of selected feature set.

• task_id (character(1)).

• learner_id (character(1)).

• resampling_id (character(1)).

Examples

# Nested Resampling on Palmer Penguins Data Set

# create auto fselector
at = auto_fselector(
  fselector = fs("random_search"),
  learner = lrn("classif.rpart"),
  resampling = rsmp ("holdout"),
  measure = msr("classif.ce"),
  term_evals = 4)

resampling_outer = rsmp("cv", folds = 2)
rr = resample(tsk("iris"), at, resampling_outer, store_models = TRUE)

# extract inner results
extract_inner_fselect_results(rr)

mlr3fselect documentation built on March 7, 2023, 5:31 p.m.