SVM-RFE Callback

Runs a recursive feature elimination with a mlr3learners::LearnerClassifSVM. The SVM must be configured with type = "C-classification" and kernel = "linear".

Source

Guyon I, Weston J, Barnhill S, Vapnik V (2002). “Gene Selection for Cancer Classification using Support Vector Machines.” Machine Learning, 46(1), 389--422. ISSN 1573-0565, doi:10.1023/A:1012487302797 .

Examples

clbk("mlr3fselect.svm_rfe")
#> <CallbackFSelect:mlr3fselect.svm_rfe>: SVM-RFE Callback
#> * Active Stages: on_optimization_begin

library(mlr3learners)

# Create instance with classification svm with linear kernel
instance = fsi(
  task = tsk("sonar"),
  learner = lrn("classif.svm", type = "C-classification", kernel = "linear"),
  resampling = rsmp("cv", folds = 3),
  measures = msr("classif.ce"),
  terminator = trm("none"),
  callbacks = clbk("mlr3fselect.svm_rfe"),
  store_models = TRUE
)

fselector = fs("rfe", feature_number = 5, n_features = 10)

# Run recursive feature elimination on the Sonar data set
fselector$optimize(instance)
#>      V1   V10   V11  V12   V13   V14   V15   V16  V17   V18  V19    V2   V20
#> 1: TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE
#>      V21   V22  V23   V24   V25   V26   V27   V28   V29    V3  V30  V31  V32
#> 1: FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE
#>      V33   V34   V35   V36  V37   V38   V39    V4  V40   V41   V42   V43   V44
#> 1: FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
#>     V45   V46   V47   V48  V49   V5  V50  V51  V52   V53   V54   V55   V56
#> 1: TRUE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE
#>      V57   V58   V59   V6   V60   V7   V8   V9                   features
#> 1: FALSE FALSE FALSE TRUE FALSE TRUE TRUE TRUE V1,V12,V17,V19,V23,V30,...
#>    classif.ce
#> 1:  0.2355418