Dictionary of Learners — mlr

'out of office hours'指非工作时间 #生活技巧# #职场沟通技巧# #商务英语#

as.data.table(mlr_learners) #> Key: <key> #> key label task_type #> <char> <char> <char> #> 1: classif.debug Debug Learner for Classification classif #> 2: classif.featureless Featureless Classification Learner classif #> 3: classif.rpart Classification Tree classif #> 4: regr.debug Debug Learner for Regression regr #> 5: regr.featureless Featureless Regression Learner regr #> 6: regr.rpart Regression Tree regr #> feature_types packages #> <list> <list> #> 1: logical,integer,numeric,character,factor,ordered mlr3 #> 2: logical,integer,numeric,character,factor,ordered,... mlr3 #> 3: logical,integer,numeric,factor,ordered mlr3,rpart #> 4: logical,integer,numeric,character,factor,ordered mlr3,stats #> 5: logical,integer,numeric,character,factor,ordered,... mlr3,stats #> 6: logical,integer,numeric,factor,ordered mlr3,rpart #> properties #> <list> #> 1: hotstart_forward,internal_tuning,marshal,missings,multiclass,twoclass,... #> 2: featureless,importance,missings,multiclass,selected_features,twoclass,... #> 3: importance,missings,multiclass,selected_features,twoclass,weights #> 4: missings,weights #> 5: featureless,importance,missings,selected_features,weights #> 6: importance,missings,selected_features,weights #> predict_types #> <list> #> 1: response,prob #> 2: response,prob #> 3: response,prob #> 4: response,se,quantiles #> 5: response,se,quantiles #> 6: response mlr_learners$get("classif.featureless") #> #> ── <LearnerClassifFeatureless> (classif.featureless): Featureless Classification #> • Model: - #> • Parameters: method=mode #> • Packages: mlr3 #> • Predict Types: [response] and prob #> • Feature Types: logical, integer, numeric, character, factor, ordered, #> POSIXct, and Date #> • Encapsulation: none (fallback: -) #> • Properties: featureless, importance, missings, multiclass, selected_features, #> twoclass, and weights #> • Other settings: use_weights = 'use' lrn("classif.rpart") #> #> ── <LearnerClassifRpart> (classif.rpart): Classification Tree ────────────────── #> • Model: - #> • Parameters: xval=0 #> • Packages: mlr3 and rpart #> • Predict Types: [response] and prob #> • Feature Types: logical, integer, numeric, factor, and ordered #> • Encapsulation: none (fallback: -) #> • Properties: importance, missings, multiclass, selected_features, twoclass, #> and weights #> • Other settings: use_weights = 'use'

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