FSelectorExhaustiveSearch class that implements an Exhaustive Search.
In order to support general termination criteria and parallelization, feature sets are evaluated in batches. The size of the feature sets is increased by 1 in each batch.
This FSelector can be instantiated via the dictionary
mlr_fselectors or with the associated sugar function
Maximum number of features. By default, number of features in mlr3::Task.
Creates a new instance of this R6 class.
The objects of this class are cloneable with this method.
FSelectorExhaustiveSearch$clone(deep = FALSE)
Whether to make a deep clone.
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library(mlr3) terminator = trm("evals", n_evals = 5) instance = FSelectInstanceSingleCrit$new( task = tsk("iris"), learner = lrn("classif.rpart"), resampling = rsmp("holdout"), measure = msr("classif.ce"), terminator = terminator ) fselector = fs("exhaustive_search") # Modifies the instance by reference fselector$optimize(instance) # Returns best scoring evaluation instance$result # Allows access of data.table of full path of all evaluations as.data.table(instance$archive)
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