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Classification tree with logistic regression models at the leaves. Calls RWeka::LMT() from RWeka.

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")

Meta Information

  • Task type: “classif”

  • Predict Types: “response”, “prob”

  • Feature Types: “integer”, “numeric”, “factor”, “ordered”

  • Required Packages: mlr3, mlr3extralearners, RWeka

Parameters

IdTypeDefaultLevelsRange
subsetuntyped--
na.actionuntyped--
BlogicalFALSETRUE, FALSE-
RlogicalFALSETRUE, FALSE-
ClogicalFALSETRUE, FALSE-
PlogicalFALSETRUE, FALSE-
Iinteger-\([1, \infty)\)
Minteger15\([1, \infty)\)
Wnumeric0\([0, 1]\)
AlogicalFALSETRUE, FALSE-
doNotMakeSplitPointActualValuelogicalFALSETRUE, FALSE-
output_debug_infologicalFALSETRUE, FALSE-
do_not_check_capabilitieslogicalFALSETRUE, FALSE-
num_decimal_placesinteger2\([1, \infty)\)
batch_sizeinteger100\([1, \infty)\)
optionsuntyped-

Parameter Changes

  • 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

References

Landwehr, Niels, Hall, Mark, Frank, Eibe (2005). “Logistic model trees.” Machine learning, 59(1), 161--205.

See also

Author

henrifnk

Super classes

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

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.

Usage


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.

Examples

learner = mlr3::lrn("classif.LMT")
print(learner)
#> <LearnerClassifLMT:classif.LMT>: Tree-based Model
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3extralearners, RWeka
#> * Predict Type: response
#> * Feature types: numeric, factor, ordered, integer
#> * Properties: multiclass, twoclass

# available parameters:
learner$param_set$ids()
#>  [1] "subset"                         "na.action"                     
#>  [3] "B"                              "R"                             
#>  [5] "C"                              "P"                             
#>  [7] "I"                              "M"                             
#>  [9] "W"                              "A"                             
#> [11] "doNotMakeSplitPointActualValue" "output_debug_info"             
#> [13] "do_not_check_capabilities"      "num_decimal_places"            
#> [15] "batch_size"                     "options"