A simple mlr3misc::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().
Format
R6::R6Class object
Usage
See mlr3misc::Dictionary.
See also
Other Filter:
Filter,
mlr_filters_anova,
mlr_filters_auc,
mlr_filters_boruta,
mlr_filters_carscore,
mlr_filters_carsurvscore,
mlr_filters_cmim,
mlr_filters_correlation,
mlr_filters_disr,
mlr_filters_find_correlation,
mlr_filters_importance,
mlr_filters_information_gain,
mlr_filters_jmi,
mlr_filters_jmim,
mlr_filters_kruskal_test,
mlr_filters_mim,
mlr_filters_mrmr,
mlr_filters_njmim,
mlr_filters_performance,
mlr_filters_permutation,
mlr_filters_relief,
mlr_filters_selected_features,
mlr_filters_univariate_cox,
mlr_filters_variance
Examples
mlr_filters$keys()
#> [1] "anova" "auc" "boruta"
#> [4] "carscore" "carsurvscore" "cmim"
#> [7] "correlation" "disr" "ensemble"
#> [10] "find_correlation" "importance" "information_gain"
#> [13] "jmi" "jmim" "kruskal_test"
#> [16] "mim" "mrmr" "njmim"
#> [19] "performance" "permutation" "relief"
#> [22] "selected_features" "univariate_cox" "variance"
as.data.table(mlr_filters)
#> Key: <key>
#> key label
#> <char> <char>
#> 1: anova ANOVA F-Test
#> 2: auc Area Under the ROC Curve Score
#> 3: boruta Boruta
#> 4: carscore Correlation-Adjusted coRrelation Score
#> 5: carsurvscore Correlation-Adjusted coRrelation Survival Score
#> 6: cmim Minimal Conditional Mutual Information Maximization
#> 7: correlation Correlation
#> 8: disr Double Input Symmetrical Relevance
#> 9: ensemble meta
#> 10: find_correlation Correlation-based Score
#> 11: importance Importance Score
#> 12: information_gain Information Gain
#> 13: jmi Joint Mutual Information
#> 14: jmim Minimal Joint Mutual Information Maximization
#> 15: kruskal_test Kruskal-Wallis Test
#> 16: mim Mutual Information Maximization
#> 17: mrmr Minimum Redundancy Maximal Relevancy
#> 18: njmim Minimal Normalized Joint Mutual Information Maximization
#> 19: performance Predictive Performance
#> 20: permutation Permutation Score
#> 21: relief RELIEF
#> 22: selected_features Embedded Feature Selection
#> 23: univariate_cox Univariate Cox Survival Score
#> 24: variance Variance
#> key label
#> <char> <char>
#> task_types task_properties
#> <list> <list>
#> 1: classif
#> 2: classif twoclass
#> 3: regr,classif
#> 4: regr
#> 5: surv
#> 6: classif,regr
#> 7: regr
#> 8: classif,regr
#> 9: classif,regr,unsupervised
#> 10: NA
#> 11: classif
#> 12: classif,regr
#> 13: classif,regr
#> 14: classif,regr
#> 15: classif
#> 16: classif,regr
#> 17: classif,regr
#> 18: classif,regr
#> 19: classif
#> 20: classif
#> 21: classif,regr
#> 22: classif
#> 23: surv
#> 24: NA
#> task_types task_properties
#> <list> <list>
#> params
#> <list>
#> 1:
#> 2:
#> 3: pValue,mcAdj,maxRuns,doTrace,holdHistory,getImp,...[8]
#> 4: lambda,diagonal,verbose
#> 5: maxIPCweight,denom
#> 6: threads
#> 7: use,method
#> 8: threads
#> 9: weights,rank_transform,filter_score_transform,result_score_transform,aggregator
#> 10: use,method
#> 11: method
#> 12: type,equal,discIntegers,threads
#> 13: threads
#> 14: threads
#> 15: na.action
#> 16: threads
#> 17: threads
#> 18: threads
#> 19: method
#> 20: standardize,nmc
#> 21: neighboursCount,sampleSize
#> 22: method
#> 23:
#> 24: na.rm
#> params
#> <list>
#> feature_types packages
#> <list> <list>
#> 1: integer,numeric stats
#> 2: integer,numeric mlr3measures
#> 3: logical,integer,numeric,factor,ordered Boruta
#> 4: logical,integer,numeric care
#> 5: integer,numeric carSurv,mlr3proba
#> 6: integer,numeric,factor,ordered praznik
#> 7: integer,numeric stats
#> 8: integer,numeric,factor,ordered praznik
#> 9: logical,integer,numeric,character,factor,ordered,...[8] mlr3pipelines
#> 10: integer,numeric stats
#> 11: logical,integer,numeric,character,factor,ordered,...[8] mlr3
#> 12: integer,numeric,factor,ordered FSelectorRcpp
#> 13: integer,numeric,factor,ordered praznik
#> 14: integer,numeric,factor,ordered praznik
#> 15: integer,numeric stats
#> 16: integer,numeric,factor,ordered praznik
#> 17: integer,numeric,factor,ordered praznik
#> 18: integer,numeric,factor,ordered praznik
#> 19: logical,integer,numeric,character,factor,ordered,...[8] mlr3,mlr3measures
#> 20: logical,integer,numeric,character,factor,ordered,...[8] mlr3,mlr3measures
#> 21: integer,numeric,factor,ordered FSelectorRcpp
#> 22: logical,integer,numeric,character,factor,ordered,...[8] mlr3
#> 23: integer,numeric,logical survival
#> 24: integer,numeric stats
#> feature_types packages
#> <list> <list>
mlr_filters$get("mim")
#>
#> ── <FilterMIM> mim: Mutual Information Maximization ────────────────────────────
#> • Task Types: classif and regr
#> • Properties: -
#> • Task Properties:
#> • Packages: praznik
#> • Feature types: integer, numeric, factor, and ordered
flt("anova")
#>
#> ── <FilterAnova> anova: ANOVA F-Test ───────────────────────────────────────────
#> • Task Types: classif
#> • Properties: -
#> • Task Properties:
#> • Packages: stats
#> • Feature types: integer and numeric