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" )
)
# 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 FALSE FALSE FALSE TRUE FALSE FALSE
features n_features classif.ce
<list> <int> <num>
1: age,glucose,pregnant 3 0.2265625
# 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