mlr3filters (version 0.4.0)

mlr_filters_permutation: Permutation Filter

Description

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.

Parameters

standardize

logical(1) Standardize feature importance by maximum score.

nmc

integer(1)

Number of Monte-Carlo iterations to use in computing the feature importance.

Super class

mlr3filters::Filter -> FilterPermutation

Public fields

learner

(mlr3::Learner)

resampling

(mlr3::Resampling)

measure

(mlr3::Measure)

Methods

Public methods

• FilterPermutation$new()

• FilterPermutation$clone()

• mlr3filters::Filter$calculate()

• mlr3filters::Filter$format()

• mlr3filters::Filter$help()

• mlr3filters::Filter$print()

Method new()

Create a FilterDISR object.

Usage

FilterPermutation$new(
  learner = mlr3::lrn("classif.rpart"),
  resampling = mlr3::rsmp("holdout"),
  measure = NULL
)

Arguments

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.

Method clone()

The objects of this class are cloneable with this method.

Usage

FilterPermutation$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

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_relief, mlr_filters_variance, mlr_filters