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Simple Decision Table majority classifier. Calls RWeka::make_Weka_classifier() from RWeka.

Initial parameter values

  • E:

    • Has only 2 out of 4 original evaluation measures : acc and auc with acc being the default

    • Reason for change: this learner should only contain evaluation measures appropriate for classification tasks

Custom mlr3 parameters

  • 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

  • P_best:

    • original id: P

  • D_best:

    • original id: D

  • N_best:

    • original id: N

  • S_best:

    • original id: S

  • 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.decision_table")
lrn("classif.decision_table")

Meta Information

  • Task type: “classif”

  • Predict Types: “response”, “prob”

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

  • Required Packages: mlr3, RWeka

Parameters

IdTypeDefaultLevelsRange
subsetuntyped--
na.actionuntyped--
ScharacterBestFirstBestFirst, GreedyStepwise-
Xinteger1\((-\infty, \infty)\)
Echaracteraccacc, auc-
Ilogical-TRUE, FALSE-
Rlogical-TRUE, FALSE-
output_debug_infologicalFALSETRUE, FALSE-
do_not_check_capabilitieslogicalFALSETRUE, FALSE-
num_decimal_placesinteger2\([1, \infty)\)
batch_sizeinteger100\([1, \infty)\)
P_bestuntyped--
D_bestcharacter10, 1, 2-
N_bestinteger-\((-\infty, \infty)\)
S_bestinteger1\((-\infty, \infty)\)
optionsuntypedNULL-

References

Kohavi R (1995). “The Power of Decision Tables.” In 8th European Conference on Machine Learning, 174--189.

See also

Author

damirpolat

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifDecisionTable

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

LearnerClassifDecisionTable$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

learner = mlr3::lrn("classif.decision_table")
print(learner)
#> <LearnerClassifDecisionTable:classif.decision_table>: Decision Table
#> * Model: -
#> * Parameters: E=acc
#> * Packages: mlr3, RWeka
#> * Predict Types:  [response], prob
#> * Feature Types: logical, integer, numeric, factor, ordered
#> * Properties: missings, multiclass, twoclass

# available parameters:
learner$param_set$ids()
#>  [1] "subset"                    "na.action"                
#>  [3] "S"                         "X"                        
#>  [5] "E"                         "I"                        
#>  [7] "R"                         "output_debug_info"        
#>  [9] "do_not_check_capabilities" "num_decimal_places"       
#> [11] "batch_size"                "P_best"                   
#> [13] "D_best"                    "N_best"                   
#> [15] "S_best"                    "options"