fsi {mlr3fselect} | R Documentation |
Syntactic Sugar for Instance Construction
Description
Function to construct a FSelectInstanceBatchSingleCrit or FSelectInstanceBatchMultiCrit.
Usage
fsi(
task,
learner,
resampling,
measures = NULL,
terminator,
store_benchmark_result = TRUE,
store_models = FALSE,
check_values = FALSE,
callbacks = NULL,
ties_method = "least_features"
)
Arguments
task |
(mlr3::Task) |
learner |
(mlr3::Learner) |
resampling |
(mlr3::Resampling) |
measures |
(mlr3::Measure or list of mlr3::Measure) |
terminator |
(bbotk::Terminator) |
store_benchmark_result |
( |
store_models |
( |
check_values |
( |
callbacks |
(list of CallbackBatchFSelect) |
ties_method |
( |
Resources
There are several sections about feature selection in the mlr3book.
Getting started with wrapper feature selection.
Do a sequential forward selection Palmer Penguins data set.
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.
Default Measures
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 |
Examples
# 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)