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.

library(mlr3verse)

# retrieve task
task = tsk("pima")

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

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

Warning in .__Codomain__maximization_to_minimization(self = self, private = private, : 'maximization_to_minimization' is deprecated.
Use 'direction' instead.
See help("Deprecated")
Warning in .__Codomain__maximization_to_minimization(self = self, private = private, : 'maximization_to_minimization' is deprecated.
Use 'direction' instead.
See help("Deprecated")
Warning in .__Codomain__maximization_to_minimization(self = self, private = private, : 'maximization_to_minimization' is deprecated.
Use 'direction' instead.
See help("Deprecated")
Warning in .__Codomain__maximization_to_minimization(self = self, private = private, : 'maximization_to_minimization' is deprecated.
Use 'direction' instead.
See help("Deprecated")
Warning in .__Codomain__maximization_to_minimization(self = self, private = private, : 'maximization_to_minimization' is deprecated.
Use 'direction' instead.
See help("Deprecated")
Warning in .__Codomain__maximization_to_minimization(self = self, private = private, : 'maximization_to_minimization' is deprecated.
Use 'direction' instead.
See help("Deprecated")
Warning in .__Codomain__maximization_to_minimization(self = self, private = private, : 'maximization_to_minimization' is deprecated.
Use 'direction' instead.
See help("Deprecated")
Warning in .__Codomain__maximization_to_minimization(self = self, private = private, : 'maximization_to_minimization' is deprecated.
Use 'direction' instead.
See help("Deprecated")

# best performing feature subset
instance$result

      age glucose insulin   mass pedigree pregnant pressure triceps
   <lgcl>  <lgcl>  <lgcl> <lgcl>   <lgcl>   <lgcl>   <lgcl>  <lgcl>
1:   TRUE    TRUE    TRUE   TRUE     TRUE     TRUE     TRUE    TRUE
                                         features n_features classif.ce
                                           <list>      <int>      <num>
1: age,glucose,insulin,mass,pedigree,pregnant,...          8  0.2148438

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

print(learner)

<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