mlr_filters_selected_features | R Documentation |
Filter using embedded feature selection of machine learning algorithms. Takes a mlr3::Learner which is capable of extracting the selected features (property "selected_features"), fits the model and extracts the selected features.
Note that contrary to mlr_filters_importance, there is no ordering in
the selected features. Selected features get a score of 1, deselected
features get a score of 0. The order of selected features is random and
different from the order in the learner. In combination with
mlr3pipelines, only the filter criterion cutoff
makes sense.
mlr3filters::Filter
-> mlr3filters::FilterLearner
-> FilterSelectedFeatures
learner
(mlr3::Learner)
Learner to extract the importance values from.
new()
Create a FilterImportance object.
FilterSelectedFeatures$new(learner = mlr3::lrn("classif.featureless"))
learner
(mlr3::Learner)
Learner to extract the selected features from.
clone()
The objects of this class are cloneable with this method.
FilterSelectedFeatures$clone(deep = FALSE)
deep
Whether to make a deep clone.
PipeOpFilter for filter-based feature selection.
Dictionary of Filters: mlr_filters
Other Filter:
Filter
,
mlr_filters
,
mlr_filters_anova
,
mlr_filters_auc
,
mlr_filters_boruta
,
mlr_filters_carscore
,
mlr_filters_carsurvscore
,
mlr_filters_cmim
,
mlr_filters_correlation
,
mlr_filters_disr
,
mlr_filters_find_correlation
,
mlr_filters_importance
,
mlr_filters_information_gain
,
mlr_filters_jmi
,
mlr_filters_jmim
,
mlr_filters_kruskal_test
,
mlr_filters_mim
,
mlr_filters_mrmr
,
mlr_filters_njmim
,
mlr_filters_performance
,
mlr_filters_permutation
,
mlr_filters_relief
,
mlr_filters_univariate_cox
,
mlr_filters_variance
if (requireNamespace("rpart")) {
task = mlr3::tsk("iris")
learner = mlr3::lrn("classif.rpart")
filter = flt("selected_features", learner = learner)
filter$calculate(task)
as.data.table(filter)
}
if (mlr3misc::require_namespaces(c("mlr3pipelines", "mlr3learners", "rpart"), quietly = TRUE)) {
library("mlr3pipelines")
library("mlr3learners")
task = mlr3::tsk("sonar")
filter = flt("selected_features", learner = lrn("classif.rpart"))
# Note: All filter scores are either 0 or 1, i.e. setting `filter.cutoff = 0.5` means that
# we select all "selected features".
graph = po("filter", filter = filter, filter.cutoff = 0.5) %>>%
po("learner", mlr3::lrn("classif.log_reg"))
graph$train(task)
}
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