This package provides feature selection for mlr3. It offers various feature selection wrappers, e.g. random search and sequential feature selection and different termination criteria can be set and combined. ‘AutoFSelect’ provides a convenient way to perform nested resampling in combination with ‘mlr3’. The package is build on bbotk which provides a common framework for optimization.
For feature filters and embedded methods, see mlr3filters
library("mlr3") library("mlr3fselect") task = tsk("pima") learner = lrn("classif.rpart") resampling = rsmp("holdout") measure = msr("classif.ce") # Define termination criterion terminator = trm("evals", n_evals = 20) # Create fselect instance instance = FSelectInstanceSingleCrit$new(task = task, learner = learner, resampling = resampling, measure = measure, terminator = terminator) # Load fselector fselector = fs("random_search") # Trigger optimization fselector$optimize(instance) # View results instance$result