mlr_fselectors_design_points | R Documentation |
Feature selection using user-defined feature sets.
The feature sets are evaluated in order as given.
The feature selection terminates itself when all feature sets are evaluated. It is not necessary to set a termination criterion.
This FSelector can be instantiated with the associated sugar function fs()
:
fs("design_points")
batch_size
integer(1)
Maximum number of configurations to try in a batch.
design
data.table::data.table
Design points to try in search, one per row.
mlr3fselect::FSelector
-> mlr3fselect::FSelectorFromOptimizer
-> FSelectorDesignPoints
new()
Creates a new instance of this R6 class.
FSelectorDesignPoints$new()
clone()
The objects of this class are cloneable with this method.
FSelectorDesignPoints$clone(deep = FALSE)
deep
Whether to make a deep clone.
Other FSelector:
mlr_fselectors_exhaustive_search
,
mlr_fselectors_genetic_search
,
mlr_fselectors_random_search
,
mlr_fselectors_rfecv
,
mlr_fselectors_rfe
,
mlr_fselectors_sequential
,
mlr_fselectors_shadow_variable_search
,
mlr_fselectors
# Feature Selection # retrieve task and load learner task = tsk("pima") learner = lrn("classif.rpart") # create design design = mlr3misc::rowwise_table( ~age, ~glucose, ~insulin, ~mass, ~pedigree, ~pregnant, ~pressure, ~triceps, TRUE, FALSE, TRUE, TRUE, FALSE, TRUE, FALSE, TRUE, TRUE, TRUE, FALSE, TRUE, FALSE, TRUE, FALSE, FALSE, TRUE, FALSE, TRUE, TRUE, FALSE, TRUE, FALSE, FALSE, TRUE, FALSE, TRUE, TRUE, FALSE, TRUE, TRUE, TRUE ) # run feature selection on the Pima Indians diabetes data set instance = fselect( fselector = fs("design_points", design = design), task = task, learner = learner, resampling = rsmp("holdout"), measure = msr("classif.ce") ) # best performing feature set instance$result # all evaluated feature sets as.data.table(instance$archive) # subset the task and fit the final model task$select(instance$result_feature_set) learner$train(task)
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