Selects the smallest feature set within one standard error of the best as the result. If there are multiple such feature sets with the same number of features, the first one is selected. If the sets have exactly the same performance but different number of features, the one with the smallest number of features is selected.
Source
Kuhn, Max, Johnson, Kjell (2013). “Applied Predictive Modeling.” In chapter Over-Fitting and Model Tuning, 61–92. Springer New York, New York, NY. ISBN 978-1-4614-6849-3.
Examples
clbk("mlr3fselect.one_se_rule")
#> <CallbackBatchFSelect:mlr3fselect.one_se_rule>: One Standard Error Rule Callback
#> * Active Stages: on_optimization_end
# Run feature selection on the pima data set with the callback
instance = fselect(
fselector = fs("random_search"),
task = tsk("pima"),
learner = lrn("classif.rpart"),
resampling = rsmp ("cv", folds = 3),
measures = msr("classif.ce"),
term_evals = 10,
callbacks = clbk("mlr3fselect.one_se_rule"))
# Smallest feature set within one standard error of the best
instance$result
#> age glucose insulin mass pedigree pregnant pressure triceps
#> <lgcl> <lgcl> <lgcl> <lgcl> <lgcl> <lgcl> <lgcl> <lgcl>
#> 1: TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE
#> features n_features classif.ce
#> <list> <list> <num>
#> 1: age,glucose,mass,pedigree,pregnant,pressure,... 7 0.2591146