Calculates the Correlation-Adjusted (marginal) coRelation scores (short CAR scores) implemented in care::carscore() in package care. The CAR scores for a set of features are defined as the correlations between the target and the decorrelated features. The filter returns the absolute value of the calculated scores.

Argument verbose defaults to FALSE.

See also

Super class

mlr3filters::Filter -> FilterCarScore

Methods

Public methods

Inherited methods

Method new()

Create a FilterCarScore object.

Usage

FilterCarScore$new(
  id = "carscore",
  task_type = "regr",
  param_set = ParamSet$new(list(ParamDbl$new("lambda", lower = 0, upper = 1, default =
    NO_DEF), ParamLgl$new("diagonal", default = FALSE), ParamLgl$new("verbose", default =
    TRUE))),
  packages = "care",
  feature_types = "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

FilterCarScore$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

task = mlr3::tsk("mtcars") filter = flt("carscore") filter$calculate(task) head(as.data.table(filter), 3)
#> feature score #> 1: wt 0.4144012 #> 2: hp 0.3174307 #> 3: cyl 0.3102745
## changing filter settings filter = flt("carscore") filter$param_set$values = list("diagonal" = TRUE) filter$calculate(task)
#> Estimating optimal shrinkage intensity lambda (correlation matrix): 0.0707
head(as.data.table(filter), 3)
#> feature score #> 1: wt 0.8062818 #> 2: cyl 0.7918806 #> 3: disp 0.7875962