Classification Bayes Network Learner

Bayes Network learning using various search algorithms. Calls RWeka::make_Weka_classifier() from RWeka.

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

• Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern

• N removed:

  • Initial structure is empty

• P removed:

  • Maximum number of parents

• R removed:

  • Random order

• mbc removed:

  • Applies a Markov Blanket correction to the network structure, after a network structure is learned

• S removed:

  • Score type

• A removed:

  • Initial count (alpha)

• Reason for change: The parameters are removed because they don't work out of the box and it's unclear how to use them.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.bayes_net")

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	-		-
D	logical	-	TRUE, FALSE	-
B	untyped	-		-
Q	character	-	global.K2, global.HillClimber, global.SimulatedAnnealing, global.TabuSearch, global.TAN, local.K2, local.HillClimber, local.LAGDHillClimber, local.SimulatedAnnealing, local.TabuSearch, ...	-
E	character	-	estimate.SimpleEstimator, estimate.BMAEstimator, estimate.MultiNomialBMAEstimator	-
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		\([1, \infty)\)
options	untyped	NULL		-

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 -> LearnerClassifBayesNet

Active bindings

marshaled

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

Methods

Public methods

• LearnerClassifBayesNet$new()

• LearnerClassifBayesNet$marshal()

• LearnerClassifBayesNet$unmarshal()

• LearnerClassifBayesNet$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

LearnerClassifBayesNet$new()

---

Method marshal()

Marshal the learner's model.

Usage

LearnerClassifBayesNet$marshal(...)

Arguments

...

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

---

Method unmarshal()

Unmarshal the learner's model.

Usage

LearnerClassifBayesNet$unmarshal(...)

Arguments

...

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

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifBayesNet$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = lrn("classif.bayes_net")
print(learner)
#> 
#> ── <LearnerClassifBayesNet> (classif.bayes_net): Bayes Network ─────────────────
#> • Model: -
#> • Parameters: list()
#> • 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)
#> Bayes Network Classifier
#> not using ADTree
#> #attributes=61 #classindex=0
#> Network structure (nodes followed by parents)
#> Class(2): 
#> V1(1): Class 
#> V10(2): Class 
#> V11(2): Class 
#> V12(2): Class 
#> V13(2): Class 
#> V14(1): Class 
#> V15(1): Class 
#> V16(1): Class 
#> V17(1): Class 
#> V18(1): Class 
#> V19(1): Class 
#> V2(1): Class 
#> V20(1): Class 
#> V21(2): Class 
#> V22(1): Class 
#> V23(1): Class 
#> V24(1): Class 
#> V25(1): Class 
#> V26(1): Class 
#> V27(1): Class 
#> V28(2): Class 
#> V29(1): Class 
#> V3(1): Class 
#> V30(1): Class 
#> V31(1): Class 
#> V32(1): Class 
#> V33(1): Class 
#> V34(1): Class 
#> V35(1): Class 
#> V36(2): Class 
#> V37(1): Class 
#> V38(1): Class 
#> V39(1): Class 
#> V4(2): Class 
#> V40(1): Class 
#> V41(1): Class 
#> V42(1): Class 
#> V43(1): Class 
#> V44(2): Class 
#> V45(2): Class 
#> V46(1): Class 
#> V47(2): Class 
#> V48(2): Class 
#> V49(2): Class 
#> V5(1): Class 
#> V50(1): Class 
#> V51(2): Class 
#> V52(2): Class 
#> V53(1): Class 
#> V54(1): Class 
#> V55(1): Class 
#> V56(1): Class 
#> V57(1): Class 
#> V58(1): Class 
#> V59(1): Class 
#> V6(1): Class 
#> V60(1): Class 
#> V7(1): Class 
#> V8(1): Class 
#> V9(2): Class 
#> LogScore Bayes: -1314.3304662697399
#> LogScore BDeu: -1341.816873329646
#> LogScore MDL: -1349.1307597366024
#> LogScore ENTROPY: -1267.7119398399461
#> LogScore AIC: -1300.7119398399461
#> 


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

# Score the predictions
predictions$score()
#> classif.ce 
#>  0.2173913