Feature Selection Wrapper

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

Example Usage

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


# 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
     age glucose insulin mass pedigree pregnant pressure triceps     features
1: FALSE    TRUE   FALSE TRUE    FALSE    FALSE    FALSE   FALSE glucose,mass
1:   0.234375
# subset the task and fit the final model

<LearnerClassifRpart:classif.rpart>: Classification Tree
* Model: rpart
* Parameters: xval=0
* Packages: mlr3, rpart
* Predict Types:  [response], prob
* Feature Types: logical, integer, numeric, factor, ordered
* Properties: importance, missings, multiclass, selected_features,
  twoclass, weights