Correlation Filter

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

See also

Dictionary of Filters: mlr_filters

Other Filter: Filter, mlr_filters_anova, mlr_filters_auc, mlr_filters_carscore, mlr_filters_cmim, mlr_filters_disr, mlr_filters_find_correlation, 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_variable_importance, mlr_filters_variance, mlr_filters

Super class

mlr3filters::Filter -> FilterCorrelation

Methods

Public methods

• FilterCorrelation$new()

• FilterCorrelation$clone()

Inherited methods

• mlr3filters::Filter$calculate()

• mlr3filters::Filter$format()

• mlr3filters::Filter$print()

---

Method new()

Create a FilterCorrelation object.

Usage

FilterCorrelation$new(
  id = "correlation",
  task_type = "regr",
  param_set = ParamSet$new(list(ParamFct$new("use", default = "everything", levels =
    c("everything", "all.obs", "complete.obs", "na.or.complete",
    "pairwise.complete.obs")), ParamFct$new("method", default = "pearson", levels =
    c("pearson", "kendall", "spearman")))),
  packages = "stats",
  feature_types = c("integer", "numeric")
)

Arguments

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.

packages

(character())
Set of required packages. Note that these packages will be loaded via requireNamespace(), and are not attached.

feature_types

(character())
Feature types the filter operates on. Must be a subset of mlr_reflections$task_feature_types.

---

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
#>  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
#>  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