fsi | R Documentation |
Function to construct a FSelectInstanceSingleCrit or FSelectInstanceMultiCrit.
fsi( task, learner, resampling, measures = NULL, terminator, store_benchmark_result = TRUE, store_models = FALSE, check_values = FALSE, callbacks = list() )
task |
(mlr3::Task) |
learner |
(mlr3::Learner) |
resampling |
(mlr3::Resampling) |
measures |
(mlr3::Measure or list of mlr3::Measure) |
terminator |
(Terminator) |
store_benchmark_result |
( |
store_models |
( |
check_values |
( |
callbacks |
(list of CallbackFSelect) |
There are several sections about feature selection in the mlr3book.
Getting started with wrapper feature selection.
The gallery features a collection of case studies and demos about optimization.
Utilize the built-in feature importance of models with Recursive Feature Elimination.
Run a feature selection with Shadow Variable Search.
Feature Selection on the Titanic data set.
If no measure is passed, the default measure is used. The default measure depends on the task type.
Task | Default Measure | Package |
"classif" | "classif.ce" | mlr3 |
"regr" | "regr.mse" | mlr3 |
"surv" | "surv.cindex" | mlr3proba |
"dens" | "dens.logloss" | mlr3proba |
"classif_st" | "classif.ce" | mlr3spatial |
"regr_st" | "regr.mse" | mlr3spatial |
"clust" | "clust.dunn" | mlr3cluster |
# Feature selection on Palmer Penguins data set task = tsk("penguins") learner = lrn("classif.rpart") # Construct feature selection instance instance = fsi( task = task, learner = learner, resampling = rsmp("cv", folds = 3), measures = msr("classif.ce"), terminator = trm("evals", n_evals = 4) ) # Choose optimization algorithm fselector = fs("random_search", batch_size = 2) # Run feature selection fselector$optimize(instance) # Subset task to optimal feature set task$select(instance$result_feature_set) # Train the learner with optimal feature set on the full data set learner$train(task) # Inspect all evaluated sets as.data.table(instance$archive)
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