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Adaptive boosting algorithm for classification. Calls RWeka::AdaBoostM1() from RWeka.

Dictionary

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

mlr_learners$get("classif.AdaBoostM1")
lrn("classif.AdaBoostM1")

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--
Pinteger100\([90, 100]\)
QlogicalFALSETRUE, FALSE-
Sinteger1\([1, \infty)\)
Iinteger10\([1, \infty)\)
WuntypedDecisionStump-
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

Freund, Yoav, Schapire, E R, others (1996). “Experiments with a new boosting algorithm.” In icml, volume 96, 148--156. Citeseer.

See also

Author

henrifnk

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifAdaBoostM1

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifAdaBoostM1$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

learner = mlr3::lrn("classif.AdaBoostM1")
print(learner)
#> <LearnerClassifAdaBoostM1:classif.AdaBoostM1>: Adaptive Boosting
#> * 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] "P"                         "Q"                        
#>  [5] "S"                         "I"                        
#>  [7] "W"                         "output_debug_info"        
#>  [9] "do_not_check_capabilities" "num_decimal_places"       
#> [11] "batch_size"                "options"