Correlation Filter

Simple correlation filter calling stats::cor(). The filter score is the absolute value of the correlation.

Note

This filter, in its default settings, can handle missing values in the features. However, the resulting filter scores may be misleading or at least difficult to compare if some features have a large proportion of missing values.

If a feature has no non-missing value, the resulting score will be NA. Missing scores appear in a random, non-deterministic order at the end of the vector of scores.

References

For a benchmark of filter methods:

Bommert A, Sun X, Bischl B, Rahnenführer J, Lang M (2020). “Benchmark for filter methods for feature selection in high-dimensional classification data.” Computational Statistics & Data Analysis, 143, 106839. doi:10.1016/j.csda.2019.106839 .

See also

• PipeOpFilter for filter-based feature selection.

• Dictionary of Filters: mlr_filters

Other Filter: Filter, mlr_filters, mlr_filters_anova, mlr_filters_auc, mlr_filters_boruta, mlr_filters_carscore, mlr_filters_carsurvscore, mlr_filters_cmim, mlr_filters_disr, mlr_filters_find_correlation, mlr_filters_importance, mlr_filters_information_gain, mlr_filters_jmi, mlr_filters_jmim, mlr_filters_kruskal_test, mlr_filters_mim, mlr_filters_mrmr, mlr_filters_njmim, mlr_filters_performance, mlr_filters_permutation, mlr_filters_relief, mlr_filters_selected_features, mlr_filters_univariate_cox, mlr_filters_variance

Super class

mlr3filters::Filter -> FilterCorrelation

Methods

Public methods

• FilterCorrelation$new()

• FilterCorrelation$clone()

Inherited methods

• mlr3filters::Filter$calculate()
• mlr3filters::Filter$format()
• mlr3filters::Filter$help()
• mlr3filters::Filter$print()

---

Method new()

Create a FilterCorrelation object.

Usage

FilterCorrelation$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

FilterCorrelation$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

## Pearson (default)
task = mlr3::tsk("mtcars")
filter = flt("correlation")
filter$calculate(task)
as.data.table(filter)
#>     feature     score
#>      <char>     <num>
#>  1:      wt 0.8676594
#>  2:     cyl 0.8521620
#>  3:    disp 0.8475514
#>  4:      hp 0.7761684
#>  5:    drat 0.6811719
#>  6:      vs 0.6640389
#>  7:      am 0.5998324
#>  8:    carb 0.5509251
#>  9:    gear 0.4802848
#> 10:    qsec 0.4186840

## Spearman
filter = FilterCorrelation$new()
filter$param_set$values = list("method" = "spearman")
filter$calculate(task)
as.data.table(filter)
#>     feature     score
#>      <char>     <num>
#>  1:     cyl 0.9108013
#>  2:    disp 0.9088824
#>  3:      hp 0.8946646
#>  4:      wt 0.8864220
#>  5:      vs 0.7065968
#>  6:    carb 0.6574976
#>  7:    drat 0.6514555
#>  8:      am 0.5620057
#>  9:    gear 0.5427816
#> 10:    qsec 0.4669358
if (mlr3misc::require_namespaces(c("mlr3pipelines", "rpart"), quietly = TRUE)) {
  library("mlr3pipelines")
  task = mlr3::tsk("boston_housing")

  # Note: `filter.frac` is selected randomly and should be tuned.

  graph = po("filter", filter = flt("correlation"), filter.frac = 0.5) %>>%
    po("learner", mlr3::lrn("regr.rpart"))

  graph$train(task)
}
#> $regr.rpart.output
#> NULL
#>