mlr_fselectors_random_search | R Documentation |
Feature selection using Random Search Algorithm.
The feature sets are randomly drawn.
The sets are evaluated in batches of size batch_size
.
Larger batches mean we can parallelize more, smaller batches imply a more fine-grained checking of termination criteria.
This FSelector can be instantiated with the associated sugar function fs()
:
fs("random_search")
max_features
integer(1)
Maximum number of features.
By default, number of features in mlr3::Task.
batch_size
integer(1)
Maximum number of feature sets to try in a batch.
mlr3fselect::FSelector
-> FSelectorRandomSearch
new()
Creates a new instance of this R6 class.
FSelectorRandomSearch$new()
clone()
The objects of this class are cloneable with this method.
FSelectorRandomSearch$clone(deep = FALSE)
deep
Whether to make a deep clone.
Bergstra J, Bengio Y (2012). “Random Search for Hyper-Parameter Optimization.” Journal of Machine Learning Research, 13(10), 281–305. https://jmlr.csail.mit.edu/papers/v13/bergstra12a.html.
Other FSelector:
mlr_fselectors_design_points
,
mlr_fselectors_exhaustive_search
,
mlr_fselectors_genetic_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("random_search"), task = task, learner = learner, resampling = rsmp("holdout"), measure = msr("classif.ce"), term_evals = 10 ) # best performing feature subset instance$result # all evaluated feature subsets as.data.table(instance$archive) # subset the task and fit the final model task$select(instance$result_feature_set) learner$train(task)
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