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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 lrn():

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

# Define the Learner
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

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
#> Decision Table:
#> 
#> Number of training instances: 139
#> Number of Rules : 30
#> Non matches covered by Majority class.
#> 	Best first.
#> 	Start set: no attributes
#> 	Search direction: forward
#> 	Stale search after 5 node expansions
#> 	Total number of subsets evaluated: 611
#> 	Merit of best subset found:   87.05 
#> Evaluation (for feature selection): CV (leave one out) 
#> Feature set: 4,35,40,43,49,51,1


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()
#> classif.ce 
#>  0.3188406