mlr_learners_multioutput.cforest: Conditional Random Forest Multioutput Learner

In mlr-org/mlr3multioutput: Multioutput extension for 'mlr3'

Description

A LearnerMultioutput implementation of the random forest and bagging ensemble algorithms utilizing conditional inference trees as base learners. Supports multilabel classification.

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

mlr_learners$get("Multioutputput.cforest")
lrn("Multioutputput.cforest")

Super classes

mlr3::Learner -> mlr3multioutput::LearnerMultioutput -> LearnerMultioutputCForest

Methods

Public methods

• LearnerMultioutputCForest$new()

• LearnerMultioutputCForest$clone()

Inherited methods

• mlr3::Learner$format()

• mlr3::Learner$help()

• mlr3::Learner$predict()

• mlr3::Learner$predict_newdata()

• mlr3::Learner$print()

• mlr3::Learner$reset()

• mlr3::Learner$train()

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerMultioutputCForest$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerMultioutputCForest$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

mlr-org/mlr3multioutput documentation built on Nov. 22, 2020, 1:17 p.m.