[SOLVED] mlr3 Multiple Measures AutoFSelector

mlr3 Multiple Measures AutoFSelector

I wanted to inquire about how to modify my code so that I could get multiple performance measures as an output.

My code is the following:


ARMSS<-read.csv("Index ARMSS Proteomics Final.csv", row.names=1)

set.seed(123, "L'Ecuyer")

task = as_task_regr(ARMSS, target = "Index.ARMSS")

learner = lrn("regr.ranger", importance = "impurity")

set_threads(learner, n = 8)

resampling_inner = rsmp("cv", folds = 7)
measure = msrs(c("regr.rmse","regr.srho"))
terminator = trm("none")

at = AutoFSelector$new(
  learner = learner,
  resampling = resampling_inner,
  measure = measure,
  terminator = terminator,
  fselect = fs("rfe", n_features = 1, feature_fraction = 0.5, recursive = FALSE),
  store_models = TRUE)

I then receive the following error:

Error in UseMethod("as_measure") : no applicable method for 'as_measure' applied to an object of class "list"

Solution

The result of multi-objective optimization is a Pareto front i.e. there are multiple best solutions. The AutoFselector needs one solution to fit the final model. Therefore, the AutoFselector only works with one measure.