mlr3filters (version 0.4.0)

mlr_filters_cmim: Minimal Conditional Mutual Information Filter

Description

Minimal conditional mutual information maximisation filter calling praznik::CMIM() from package praznik.

This filter supports partial scoring (see Filter).

Super class

mlr3filters::Filter -> FilterCMIM

Methods

Public methods

• FilterCMIM$new()

• FilterCMIM$clone()

• mlr3filters::Filter$calculate()

• mlr3filters::Filter$format()

• mlr3filters::Filter$help()

• mlr3filters::Filter$print()

Method new()

Create a FilterCMIM object.

Usage

FilterCMIM$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

FilterCMIM$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Details

As the scores calculated by the praznik package are not monotone due to the greedy forward fashion, the returned scores simply reflect the selection order: 1, (k-1)/k, ..., 1/k where k is the number of selected features.

See Also

Dictionary of Filters: mlr_filters

Other Filter: Filter, mlr_filters_anova, mlr_filters_auc, mlr_filters_carscore, 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_relief, mlr_filters_variance, mlr_filters

Examples

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