Feature Selection via Sequential Selection
FSelectorSequential class that implements sequential feature selection. The
sequential forward selection (strategy = fsf) extends the feature set in
each step with the feature that increases the models performance the most.
The sequential backward selection (strategy = fsb) starts with the complete
future set and removes in each step the feature that decreases the models
performance the least.
This FSelector can be instantiated via the dictionary
mlr_fselectors or with the associated sugar function fs():
mlr_fselectors$get("sequential")
fs("sequential")max_featuresinteger(1)
Maximum number of features. By default, number of features in mlr3::Task.
strategycharacter(1)
Search method sfs (forward search) or sbs (backward search).
mlr3fselect::FSelector -> FSelectorSequential
new()
Creates a new instance of this R6 class.'
FSelectorSequential$new()
optimization_path()
Returns the optimization path.
FSelectorSequential$optimization_path(inst)
inst(FSelectInstanceSingleCrit)
Instance optimized with FSelectorSequential.
clone()
The objects of this class are cloneable with this method.
FSelectorSequential$clone(deep = FALSE)
deepWhether to make a deep clone.
Feature sets are evaluated in batches, where each batch is one step in the sequential feature selection.
library(mlr3)
terminator = trm("evals", n_evals = 5)
instance = FSelectInstanceSingleCrit$new(
task = tsk("iris"),
learner = lrn("classif.rpart"),
resampling = rsmp("holdout"),
measure = msr("classif.ce"),
terminator = terminator
)
fselector = fs("sequential")
# Modifies the instance by reference
fselector$optimize(instance)
# Returns best scoring evaluation
instance$result
# Allows access of data.table of full path of all evaluations
as.data.table(instance$archive)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.