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.

## See also

PipeOpFilter for filter-based feature selection.

Other Filter:
`Filter`

,
`mlr_filters_anova`

,
`mlr_filters_auc`

,
`mlr_filters_carscore`

,
`mlr_filters_carsurvscore`

,
`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_selected_features`

,
`mlr_filters_variance`

,
`mlr_filters`

## Super class

`mlr3filters::Filter`

-> `FilterPermutation`

## Methods

## Inherited methods

### Method `new()`

Create a FilterPermutation object.

#### Usage

```
FilterPermutation$new(
learner = mlr3::lrn("classif.featureless"),
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.

## Examples

```
if (requireNamespace("rpart")) {
learner = mlr3::lrn("classif.rpart")
resampling = mlr3::rsmp("holdout")
measure = mlr3::msr("classif.acc")
filter = flt("permutation", learner = learner, measure = measure, resampling = resampling,
nmc = 2)
task = mlr3::tsk("iris")
filter$calculate(task)
as.data.table(filter)
}
#> feature score
#> 1: Petal.Width 1.0
#> 2: Sepal.Length 1.0
#> 3: Sepal.Width 0.6
#> 4: Petal.Length 0.2
if (mlr3misc::require_namespaces(c("mlr3pipelines", "rpart"), quietly = TRUE)) {
library("mlr3pipelines")
task = mlr3::tsk("iris")
# Note: `filter.frac` is selected randomly and should be tuned.
graph = po("filter", filter = flt("permutation", nmc = 2), filter.frac = 0.5) %>>%
po("learner", mlr3::lrn("classif.rpart"))
graph$train(task)
}
#> $classif.rpart.output
#> NULL
#>
```