mlr_fselectors_genetic_search | R Documentation |
Feature selection using the Genetic Algorithm from the package genalg.
This FSelector can be instantiated with the associated sugar function fs()
:
fs("genetic_search")
For the meaning of the control parameters, see genalg::rbga.bin()
.
genalg::rbga.bin()
internally terminates after iters
iteration.
We set ìters = 100000
to allow the termination via our terminators.
If more iterations are needed, set ìters
to a higher value in the parameter set.
mlr3fselect::FSelector
-> FSelectorGeneticSearch
new()
Creates a new instance of this R6 class.
FSelectorGeneticSearch$new()
clone()
The objects of this class are cloneable with this method.
FSelectorGeneticSearch$clone(deep = FALSE)
deep
Whether to make a deep clone.
Other FSelector:
mlr_fselectors_design_points
,
mlr_fselectors_exhaustive_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("penguins") learner = lrn("classif.rpart") # run feature selection on the Palmer Penguins data set instance = fselect( fselector = fs("genetic_search"), task = task, learner = learner, resampling = rsmp("holdout"), measure = msr("classif.ce"), term_evals = 10 ) # 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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