The permutation filter randomly permutes the values of a single feature in a
mlr3::Task to break the association with the response. The permutated
feature, together with the unmodified features, is used to perform a
mlr3::resample(). The permutation filter score is the difference between
the aggregated performance of the mlr3::Measure and the performance
estimated on the unmodified mlr3::Task.
standardizelogical(1)
Standardize feature importance by maximum score.
nmcinteger(1)
mlr3filters::Filter -> FilterPermutation
learnerresamplingmeasurenew()Create a FilterDISR object.
FilterPermutation$new(
id = "permutation",
task_type = learner$task_type,
param_set = ParamSet$new(list(ParamLgl$new("standardize", default = FALSE),
ParamInt$new("nmc", default = 50L))),
feature_types = learner$feature_types,
learner = mlr3::lrn("classif.rpart"),
resampling = mlr3::rsmp("holdout"),
measure = mlr3::msr("classif.ce")
)id(character(1))
Identifier for the filter.
task_type(character())
Types of the task the filter can operator on. E.g., "classif" or
"regr".
param_set(paradox::ParamSet) Set of hyperparameters.
feature_types(character())
Feature types the filter operates on.
Must be a subset of
mlr_reflections$task_feature_types.
learner(mlr3::Learner) mlr3::Learner to use for model fitting.
resampling(mlr3::Resampling) mlr3::Resampling to be used within resampling.
measure(mlr3::Measure) mlr3::Measure to be used for evaluating the performance.
clone()The objects of this class are cloneable with this method.
FilterPermutation$clone(deep = FALSE)
deepWhether to make a deep clone.
Dictionary of Filters: mlr_filters
Other Filter:
Filter,
mlr_filters_anova,
mlr_filters_auc,
mlr_filters_carscore,
mlr_filters_cmim,
mlr_filters_correlation,
mlr_filters_disr,
mlr_filters_find_correlation,
mlr_filters_importance,
mlr_filters_information_gain,
mlr_filters_jmim,
mlr_filters_jmi,
mlr_filters_kruskal_test,
mlr_filters_mim,
mlr_filters_mrmr,
mlr_filters_njmim,
mlr_filters_performance,
mlr_filters_variance,
mlr_filters