Classification Multinomial Naive Bayes Learner From Weka

Multinomial Naive Bayes classifier. 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

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.naive_bayes_multinomial")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

• Feature Types: “integer”, “numeric”

• Required Packages: mlr3, RWeka

Parameters

Id	Type	Default	Levels	Range
subset	untyped	-		-
na.action	untyped	-		-
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		-

References

Mccallum A, Nigam K (1998). “A Comparison of Event Models for Naive Bayes Text Classification.” In AAAI-98 Workshop on 'Learning for Text Categorization'.

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 -> LearnerClassifNaiveBayesMultinomial

Methods

Public methods

• LearnerClassifNaiveBayesMultinomial$new()

• LearnerClassifNaiveBayesMultinomial$clone()

Inherited methods

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

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifNaiveBayesMultinomial$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifNaiveBayesMultinomial$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = mlr3::lrn("classif.naive_bayes_multinomial")
print(learner)
#> <LearnerClassifNaiveBayesMultinomial:classif.naive_bayes_multinomial>: Multinomial Naive Bayes
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, RWeka
#> * Predict Types:  [response], prob
#> * Feature Types: integer, numeric
#> * Properties: 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)
#> The independent probability of a class
#> --------------------------------------
#> M	0.52
#> R	0.48
#> 
#> The probability of a word given the class
#> -----------------------------------------
#> 	M	R	
#> V1	0	0	
#> V10	0.01	0.01	
#> V11	0.02	0.01	
#> V12	0.02	0.01	
#> V13	0.02	0.01	
#> V14	0.02	0.02	
#> V15	0.02	0.02	
#> V16	0.02	0.02	
#> V17	0.02	0.03	
#> V18	0.03	0.03	
#> V19	0.03	0.03	
#> V2	0	0	
#> V20	0.03	0.03	
#> V21	0.04	0.03	
#> V22	0.04	0.03	
#> V23	0.04	0.04	
#> V24	0.04	0.04	
#> V25	0.04	0.04	
#> V26	0.04	0.04	
#> V27	0.04	0.04	
#> V28	0.04	0.04	
#> V29	0.04	0.04	
#> V3	0	0	
#> V30	0.03	0.03	
#> V31	0.03	0.03	
#> V32	0.02	0.03	
#> V33	0.02	0.03	
#> V34	0.02	0.03	
#> V35	0.02	0.03	
#> V36	0.02	0.03	
#> V37	0.02	0.03	
#> V38	0.02	0.02	
#> V39	0.02	0.02	
#> V4	0	0	
#> V40	0.02	0.02	
#> V41	0.02	0.02	
#> V42	0.02	0.02	
#> V43	0.02	0.01	
#> V44	0.01	0.01	
#> V45	0.01	0.01	
#> V46	0.01	0.01	
#> V47	0.01	0.01	
#> V48	0.01	0	
#> V49	0	0	
#> V5	0.01	0	
#> V50	0	0	
#> V51	0	0	
#> V52	0	0	
#> V53	0	0	
#> V54	0	0	
#> V55	0	0	
#> V56	0	0	
#> V57	0	0	
#> V58	0	0	
#> V59	0	0	
#> V6	0.01	0.01	
#> V60	0	0	
#> V7	0.01	0.01	
#> V8	0.01	0.01	
#> V9	0.01	0.01	
#> 


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

# Score the predictions
predictions$score()
#> classif.ce 
#>  0.2898551