Feature Selection Wrapper

Feature selection algorithms of the mlr3 ecosystem.

Feature selection wrappers can be found in the mlr3fselect packages. The goal is to find the best subset of features w.r.t. a performance measure in an iterative fashion.

Example Usage

Run a sequential feature selection on the Pima Indian Diabetes data set.

library(mlr3verse)

# retrieve task
task = tsk("pima")

# load learner
learner = lrn("classif.rpart")

# feature selection on the pima indians diabetes data set
instance = fselect(
  method = fs("sequential"),
  task = task,
  learner = learner,
  resampling = rsmp("holdout"),
  measure = msr("classif.ce")
)

# best performing feature subset
instance$result

     age glucose insulin mass pedigree pregnant pressure triceps
1: FALSE    TRUE   FALSE TRUE    FALSE     TRUE    FALSE   FALSE
                features classif.ce
1: glucose,mass,pregnant  0.2773438

# subset the task and fit the final model
task$select(instance$result_feature_set)
learner$train(task)