Classification Decision Table Learner

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

Id	Type	Default	Levels	Range
subset	untyped	-		-
na.action	untyped	-		-
S	character	BestFirst	BestFirst, GreedyStepwise	-
X	integer	1		\((-\infty, \infty)\)
E	character	acc	acc, auc	-
I	logical	-	TRUE, FALSE	-
R	logical	-	TRUE, FALSE	-
output_debug_info	logical	FALSE	TRUE, FALSE	-
do_not_check_capabilities	logical	FALSE	TRUE, FALSE	-
num_decimal_places	integer	2		\([1, \infty)\)
batch_size	integer	100		\([1, \infty)\)
P_best	untyped	-		-
D_best	character	1	0, 1, 2	-
N_best	integer	-		\((-\infty, \infty)\)
S_best	integer	1		\((-\infty, \infty)\)
options	untyped	NULL		-

References

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

See also

• Dictionary of Learners: mlr3::mlr_learners.

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners

• mlr3learners for a selection of recommended learners.

• mlr3cluster for unsupervised clustering learners.

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Author

damirpolat

Super classes

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

Active bindings

marshaled

(logical(1))
Whether the learner has been marshaled.

Methods

Public methods

• LearnerClassifDecisionTable$new()

• LearnerClassifDecisionTable$marshal()

• LearnerClassifDecisionTable$unmarshal()

• LearnerClassifDecisionTable$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$configure()
• mlr3::Learner$encapsulate()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$selected_features()
• mlr3::Learner$train()
• mlr3::LearnerClassif$predict_newdata_fast()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifDecisionTable$new()

---

Method marshal()

Marshal the learner's model.

Usage

LearnerClassifDecisionTable$marshal(...)

Arguments

...

(any)
Additional arguments passed to mlr3::marshal_model().

---

Method unmarshal()

Unmarshal the learner's model.

Usage

LearnerClassifDecisionTable$unmarshal(...)

Arguments

...

(any)
Additional arguments passed to mlr3::unmarshal_model().

---

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 = lrn("classif.decision_table")
print(learner)
#> 
#> ── <LearnerClassifDecisionTable> (classif.decision_table): Decision Table ──────
#> • Model: -
#> • Parameters: E=acc
#> • Packages: mlr3 and RWeka
#> • Predict Types: [response] and prob
#> • Feature Types: logical, integer, numeric, factor, and ordered
#> • Encapsulation: none (fallback: -)
#> • Properties: marshal, missings, multiclass, and twoclass
#> • Other settings: use_weights = 'error'

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

# Create train and test set
ids = 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 : 20
#> 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: 561
#> 	Merit of best subset found:   82.734
#> Evaluation (for feature selection): CV (leave one out) 
#> Feature set: 4,15,40,44,51,1


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

# Score the predictions
predictions$score()
#> classif.ce 
#>  0.2608696