mlr3filters (version 0.3.0)

mlr_filters_mim: Conditional Mutual Information Based Feature Selection Filter

Description

Conditional mutual information based feature selection filter calling praznik::MIM() in package praznik.

This filter supports partial scoring (see Filter).

Super class

mlr3filters::Filter -> FilterMIM

Methods

Public methods

• FilterMIM$new()

• FilterMIM$clone()

• mlr3filters::Filter$calculate()

• mlr3filters::Filter$format()

• mlr3filters::Filter$help()

• mlr3filters::Filter$print()

Method new()

Create a FilterMIM object.

Usage

FilterMIM$new(
  id = "mim",
  task_type = "classif",
  param_set = ParamSet$new(list(ParamInt$new("threads", lower = 0L, default = 0L))),
  packages = "praznik",
  feature_types = c("integer", "numeric", "factor", "ordered")
)

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

FilterMIM$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_carscore, 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_mrmr, mlr_filters_njmim, mlr_filters_performance, mlr_filters_permutation, mlr_filters_variance, mlr_filters

Examples

# NOT RUN {
task = mlr3::tsk("iris")
filter = flt("mim")
filter$calculate(task, nfeat = 2)
as.data.table(filter)
# }