extract_inner_fselect_results {mlr3fselect} | R Documentation |
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 |
|
fselect_instance |
( |
... |
(any) |
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 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)