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instance$result
.ties_method
options "least_features"
and "random"
to ArchiveFSelect$best()
.ArchiveFSelect$best()
method.FSelectorRFE
.as.data.table.ArchiveFSelect()
.always_include
column role.$phash()
method to AutoFSelector
.FSelector
in hash of AutoFSelector
.FSelectorRandomSearch
to 10.batch_size
parameter to FSelectorExhaustiveSearch
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.ArchiveFSelect()
function additionally returns a character vector of selected features for each row.callbacks
argument to fsi()
function.mlr3pipelines
.genalg
to required packages of FSelectorGeneticSearch
.callback_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 FSelectInstanceSingleCrit
or FSelectInstanceMultiCrit
.unnest
option from as.data.table.ArchiveFSelect()
function.FSelector
objects have the field $id
now.FSelector
objects as method
in fselect()
and auto_fselector()
.$label
to FSelector
s.fselect()
function.$help()
method which opens manual page of a FSelector
.as.data.table.DictionaryFSelector
function.min_features
parameter to FSelectorSequential
.store_models
flag to fselect()
.store_x_domain
flag.AutoFSelector$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.ArchiveFSelect$data
is a public field now.AutoFSelector$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.FSelectorGeneticSearch
.check_values
flag in FSelectInstanceSingleCrit
and FSelectInstanceMultiCrit
.bibtex
.PipeOpSelect
is internally used for task subsetting.Archive
is ArchiveFSelect
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.