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().
See also
Other Filter:
Filter,
mlr_filters_anova,
mlr_filters_auc,
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_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_selected_features,
mlr_filters_variance
Examples
mlr_filters$keys()
#> [1] "anova" "auc" "carscore"
#> [4] "carsurvscore" "cmim" "correlation"
#> [7] "disr" "find_correlation" "importance"
#> [10] "information_gain" "jmi" "jmim"
#> [13] "kruskal_test" "mim" "mrmr"
#> [16] "njmim" "performance" "permutation"
#> [19] "relief" "selected_features" "variance"
as.data.table(mlr_filters)
#> key label
#> 1: anova ANOVA F-Test
#> 2: auc Area Under the ROC Curve Score
#> 3: carscore Correlation-Adjusted coRrelation Score
#> 4: carsurvscore Correlation-Adjusted coRrelation Survival Score
#> 5: cmim Minimal Conditional Mutual Information Maximization
#> 6: correlation Correlation
#> 7: disr Double Input Symmetrical Relevance
#> 8: find_correlation Correlation-based Score
#> 9: importance Importance Score
#> 10: information_gain Information Gain
#> 11: jmi Joint Mutual Information
#> 12: jmim Minimal Joint Mutual Information Maximization
#> 13: kruskal_test Kruskal-Wallis Test
#> 14: mim Mutual Information Maximization
#> 15: mrmr Minimum Redundancy Maximal Relevancy
#> 16: njmim Minimal Normalised Joint Mutual Information Maximization
#> 17: performance Predictive Performance
#> 18: permutation Permutation Score
#> 19: relief RELIEF
#> 20: selected_features Embedded Feature Selection
#> 21: variance Variance
#> key label
#> task_type task_properties params
#> 1: classif
#> 2: classif twoclass
#> 3: regr lambda,diagonal,verbose
#> 4: surv maxIPCweight,denom
#> 5: classif,regr threads
#> 6: regr use,method
#> 7: classif,regr threads
#> 8: classif,regr use,method
#> 9: classif method
#> 10: classif,regr type,equal,discIntegers,threads
#> 11: classif,regr threads
#> 12: classif,regr threads
#> 13: classif na.action
#> 14: classif,regr threads
#> 15: classif,regr threads
#> 16: classif,regr threads
#> 17: classif method
#> 18: classif standardize,nmc
#> 19: classif,regr neighboursCount,sampleSize
#> 20: classif method
#> 21: NA na.rm
#> task_type task_properties params
#> feature_types packages
#> 1: integer,numeric stats
#> 2: integer,numeric mlr3measures
#> 3: numeric care
#> 4: integer,numeric carSurv,mlr3proba
#> 5: integer,numeric,factor,ordered praznik
#> 6: integer,numeric stats
#> 7: integer,numeric,factor,ordered praznik
#> 8: integer,numeric stats
#> 9: logical,integer,numeric,character,factor,ordered,... mlr3
#> 10: integer,numeric,factor,ordered FSelectorRcpp
#> 11: integer,numeric,factor,ordered praznik
#> 12: integer,numeric,factor,ordered praznik
#> 13: integer,numeric stats
#> 14: integer,numeric,factor,ordered praznik
#> 15: integer,numeric,factor,ordered praznik
#> 16: integer,numeric,factor,ordered praznik
#> 17: logical,integer,numeric,character,factor,ordered,... mlr3,mlr3measures
#> 18: logical,integer,numeric,character,factor,ordered,... mlr3,mlr3measures
#> 19: integer,numeric,factor,ordered FSelectorRcpp
#> 20: logical,integer,numeric,character,factor,ordered,... mlr3
#> 21: integer,numeric stats
#> feature_types packages
mlr_filters$get("mim")
#> <FilterMIM:mim>: Mutual Information Maximization
#> Task Types: classif, regr
#> Task Properties: -
#> Packages: praznik
#> Feature types: integer, numeric, factor, ordered
flt("anova")
#> <FilterAnova:anova>: ANOVA F-Test
#> Task Types: classif
#> Task Properties: -
#> Packages: stats
#> Feature types: integer, numeric