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mlr3fselect.internal_tuning.mlr_reflections$loaded_packages field.BenchmarkResult in
ObjectiveFSelectBatch after optimization.x_domain column from archive.ensemble_fselect().FSelector class is
FSelectorBatch now.FSelectInstanceSingleCrit and
FSelectInstanceMultiCrit classes are
FSelectInstanceBatchSingleCrit and
FSelectInstanceBatchMultiCrit now.CallbackFSelect class is
CallbackBatchFSelect now.ContextEval class is
ContextBatchFSelect now.instance$result.ties_method options
"least_features" and "random" to
ArchiveBatchFSelect$best().ArchiveBatchFSelect$best() method.FSelectorRFE.as.data.table.ArchiveBatchFSelect().always_include column role.$phash() method to
AutoFSelector.FSelector in hash of
AutoFSelector.FSelectorBatchRandomSearch to 10.batch_size parameter to
FSelectorBatchExhaustiveSearch to reduce memory
consumption.method parameter of
fselect(), fselect_nested() and
auto_fselector() is renamed to fselector. Only
FSelector objects are accepted now. Arguments to the
fselector cannot be passed with ... anymore.fselect parameter of
FSelector is moved to the first position to achieve
consistency with the other functions.mlr3fselect.svm_rfe to run recursive
feature elimination on linear support vector machines.FSelectorRFE are now
aggregated by rank instead of averaging them.FSelectorRFECV optimizer to run recursive
feature elimination with cross-validation.FSelectorRFE works without
store_models = TRUE now.as.data.table.ArchiveBatchFSelect() function
additionally returns a character vector of selected features for each
row.callbacks argument to fsi()
function.mlr3pipelines.genalg to required packages of
FSelectorBatchGeneticSearch.callback_batch_fselect() function.FSelectorRFE throws an error if the learner
does not support the $importance() method.AutoFSelector stores the instance and
benchmark result if store_models = TRUE.AutoFSelector stores the instance if
store_benchmark_result = TRUE.AutoFSelector to
auto_fselect().fsi() function to create a
FSelectInstanceBatchSingleCrit or
FSelectInstanceBatchMultiCrit.unnest option from
as.data.table.ArchiveBatchFSelect() function.FSelector objects have the field $id
now.FSelector objects as
method in fselect() and
auto_fselector().$label to FSelectors.fselect() function.$help() method which opens manual page of a
FSelector.as.data.table.DictionaryFSelector
function.min_features parameter to
FSelectorBatchSequential.store_models flag to fselect().store_x_domain flag.AutoFSelector$base_learner() method to extract the
base learner from nested learner objects.fselect(), auto_fselector() and
fselect_nested() sugar functions.extract_inner_fselect_results() and
extract_inner_fselect_archives() helper function to extract
inner feature selection results and archives.x_domain column from archive.FSelectorRFE stores importance values of each evaluated
feature set in archive.ArchiveBatchFSelect$data is a public field now.AutoFSelector$predict()FSelectorRFE supports fraction of features to retain in
each iteration (feature_fraction), number of features to
remove in each iteration (feature_number) and vector of
number of features to retain in each iteration
(subset_sizes).AutoFSelect is renamed to
AutoFSelector.as.data.table(rr)$learner[[1]]$fselect_result
must be used now.store_benchmark_result,
store_models and check_values in
AutoFSelector. store_fselect_instance must be
set as a parameter during initialization.FSelectorBatchGeneticSearch.check_values flag in
FSelectInstanceBatchSingleCrit and
FSelectInstanceBatchMultiCrit.bibtex.PipeOpSelect is internally used for task
subsetting.Archive is ArchiveBatchFSelect now which
stores the benchmark result in $benchmark_result. This
change removed the resample results from the archive but they can be
still accessed via the benchmark result.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.