Dictionary of Filters

Source: R/mlr_filters.R

A simple Dictionary storing objects of class Filter. Each Filter has an associated help page, see mlr_filters_[id].

This dictionary can get populated with additional filters by add-on packages.

For a more convenient way to retrieve and construct filters, see flt().

mlr_filters

Format

R6Class object

Usage

See Dictionary.

See also

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_mim, mlr_filters_mrmr, mlr_filters_njmim, mlr_filters_performance, mlr_filters_variance

Examples

mlr_filters$keys()
#>  [1] "anova"            "auc"              "carscore"         "cmim"            
#>  [5] "correlation"      "disr"             "find_correlation" "importance"      
#>  [9] "information_gain" "jmi"              "jmim"             "kruskal_test"    
#> [13] "mim"              "mrmr"             "njmim"            "performance"     
#> [17] "variance"        
as.data.table(mlr_filters)
#>                  key    task_type task_properties  param_set
#>  1:            anova      classif                 <ParamSet>
#>  2:              auc      classif        twoclass <ParamSet>
#>  3:         carscore         regr                 <ParamSet>
#>  4:             cmim classif,regr                 <ParamSet>
#>  5:      correlation         regr                 <ParamSet>
#>  6:             disr      classif                 <ParamSet>
#>  7: find_correlation classif,regr                 <ParamSet>
#>  8:       importance      classif                 <ParamSet>
#>  9: information_gain classif,regr                 <ParamSet>
#> 10:              jmi      classif                 <ParamSet>
#> 11:             jmim      classif                 <ParamSet>
#> 12:     kruskal_test      classif                 <ParamSet>
#> 13:              mim      classif                 <ParamSet>
#> 14:             mrmr      classif                 <ParamSet>
#> 15:            njmim      classif                 <ParamSet>
#> 16:      performance      classif                 <ParamSet>
#> 17:         variance classif,regr                 <ParamSet>
#>                              feature_types      packages
#>  1:                        integer,numeric         stats
#>  2:                        integer,numeric  mlr3measures
#>  3:                                numeric          care
#>  4:         integer,numeric,factor,ordered       praznik
#>  5:                        integer,numeric         stats
#>  6:         integer,numeric,factor,ordered       praznik
#>  7:                        integer,numeric         stats
#>  8: logical,integer,numeric,factor,ordered         rpart
#>  9:         integer,numeric,factor,ordered FSelectorRcpp
#> 10:         integer,numeric,factor,ordered       praznik
#> 11:         integer,numeric,factor,ordered       praznik
#> 12:                        integer,numeric         stats
#> 13:         integer,numeric,factor,ordered       praznik
#> 14:         integer,numeric,factor,ordered       praznik
#> 15:         integer,numeric,factor,ordered       praznik
#> 16: logical,integer,numeric,factor,ordered              
#> 17:                        integer,numeric         stats
mlr_filters$get("mim")
#> <FilterMIM:mim>
#> Task Types: classif
#> Task Properties: -
#> Packages: praznik
#> Feature types: integer, numeric, factor, ordered
flt("anova")
#> <FilterAnova:anova>
#> Task Types: classif
#> Task Properties: -
#> Packages: stats
#> Feature types: integer, numeric