mlr3filters (version 0.3.0)

mlr_filters_carscore: Conditional Mutual Information Based Feature Selection Filter

Description

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.

Super class

mlr3filters::Filter -> FilterCarScore

Methods

Public methods

• FilterCarScore$new()

• FilterCarScore$clone()

• mlr3filters::Filter$calculate()

• mlr3filters::Filter$format()

• mlr3filters::Filter$help()

• mlr3filters::Filter$print()

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.

See Also

Dictionary of Filters: mlr_filters

Other Filter: Filter, mlr_filters_anova, mlr_filters_auc, 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_permutation, mlr_filters_variance, mlr_filters

Examples

# NOT RUN {
task = mlr3::tsk("mtcars")
filter = flt("carscore")
filter$calculate(task)
head(as.data.table(filter), 3)

## changing filter settings
filter = flt("carscore")
filter$param_set$values = list("diagonal" = TRUE)
filter$calculate(task)
head(as.data.table(filter), 3)
# }