mlr_learners_classif.LMT: Classification Logistic Model Trees Learner

In henrifnk/mlr3learners.RWeka: AdaBoostM1, IBk ,JRip, J48, LMT, M5Rules, OneR and PART Learner for mlr3

Description

A mlr3::LearnerClassif implementing classification LMT from package RWeka.

Custom mlr3 defaults

• output_debug_info:

  • original id: output-debug-info

• do_not_check_capabilities:

  • original id: do-not-check-capabilities

• num_decimal_places:

  • original id: num-decimal-places

• batch_size:

  • original id: batch-size

• Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern

Dictionary

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

mlr_learners$get("classif.LMT")
lrn("classif.LMT")

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifLMT

Methods

Public methods

• LearnerClassifLMT$new()

• LearnerClassifLMT$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

LearnerClassifLMT$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifLMT$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

References

Landwehr N, Hall M, Frank E (2005). Logistic Model Trees https://link.springer.com/content/pdf/10.1007/s10994-005-0466-3.pdf

henrifnk/mlr3learners.RWeka documentation built on June 8, 2020, 5:54 a.m.