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Lists all learners, properties, and associated packages in a table that can be filtered and queried.

Usage

list_mlr3learners(select = NULL, filter = NULL)

Arguments

select

character()
Passed to data.table::subset.

filter

list()
Named list of conditions to filter on, names correspond to column names in table.

Examples

list_mlr3learners(
  select = c("id", "properties", "predict_types"),
  filter = list(class = "surv", predict_types = "distr"))
#> This will take a few seconds.
#> obliqueRSF has been superseded by aorsf. We highly recommend you use aorsf to fit oblique random survival forests: see https://github.com/bcjaeger/aorsf or install from CRAN with install.packages('aorsf')
#>                   id                                    properties
#>  1:     surv.akritas                                              
#>  2:       surv.aorsf                          importance,oob_error
#>  3:  surv.blackboost                                       weights
#>  4:     surv.cforest                                       weights
#>  5:    surv.coxboost                                       weights
#>  6:       surv.coxph                                       weights
#>  7:     surv.coxtime                                              
#>  8:       surv.ctree                                       weights
#>  9: surv.cv_coxboost                                       weights
#> 10:     surv.deephit                                              
#> 11:    surv.deepsurv                                              
#> 12:     surv.dnnsurv                                              
#> 13:    surv.flexible                                       weights
#> 14:    surv.gamboost          importance,selected_features,weights
#> 15:    surv.glmboost                                       weights
#> 16:      surv.kaplan                                      missings
#> 17:      surv.loghaz                                              
#> 18:      surv.mboost          importance,selected_features,weights
#> 19:      surv.nelson                                      missings
#> 20:  surv.obliqueRSF                            missings,oob_error
#> 21:  surv.parametric                                       weights
#> 22:    surv.pchazard                                              
#> 23:   surv.penalized                                              
#> 24:      surv.ranger                  importance,oob_error,weights
#> 25:       surv.rfsrc         importance,missings,oob_error,weights
#> 26:       surv.rpart importance,missings,selected_features,weights
#>                   id                                    properties
#>      predict_types
#>  1:    crank,distr
#>  2:    crank,distr
#>  3: crank,distr,lp
#>  4:    crank,distr
#>  5: crank,distr,lp
#>  6: crank,distr,lp
#>  7:    crank,distr
#>  8:    crank,distr
#>  9: crank,distr,lp
#> 10:    crank,distr
#> 11:    crank,distr
#> 12:    crank,distr
#> 13: crank,distr,lp
#> 14: crank,distr,lp
#> 15: crank,distr,lp
#> 16:    crank,distr
#> 17:    crank,distr
#> 18: crank,distr,lp
#> 19:    crank,distr
#> 20:    crank,distr
#> 21: crank,distr,lp
#> 22:    crank,distr
#> 23:    crank,distr
#> 24:    crank,distr
#> 25:    crank,distr
#> 26:    crank,distr
#>      predict_types