DWFuzzyHipergeometricNaiveBayesModel: Double Weighted Fuzzy Hypergeometric Naive Bayes (R6 class)

In FuzzyClass: Fuzzy and Non-Fuzzy Classifiers

Double Weighted Fuzzy Hypergeometric Naive Bayes (R6 class)

Description

R6 implementation of the Double Weighted Fuzzy Hypergeometric Naive Bayes classifier. Create the model with DWFuzzyHipergeometricNaiveBayesModel$new(), train it with ⁠$fit(train, cl)⁠ and classify new observations with ⁠$predict(newdata)⁠. The functional interface DWFuzzyHipergeometricNaiveBayes() trains and returns an object of this class.

Super class

FuzzyClassifier -> DWFuzzyHipergeometricNaiveBayes

Methods

Public methods

• DWFuzzyHipergeometricNaiveBayes$new()

• DWFuzzyHipergeometricNaiveBayes$clone()

Inherited methods

• FuzzyClassifier$fit()
• FuzzyClassifier$is_fitted()
• FuzzyClassifier$predict()
• FuzzyClassifier$print()
• FuzzyClassifier$set_params()

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DWFuzzyHipergeometricNaiveBayes$new()

Create a new (untrained) classifier.

Usage

DWFuzzyHipergeometricNaiveBayes$new(fuzzy = TRUE, wdelta, weta)

Arguments

fuzzy

See DWFuzzyHipergeometricNaiveBayes().

wdelta

See DWFuzzyHipergeometricNaiveBayes().

weta

See DWFuzzyHipergeometricNaiveBayes().

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DWFuzzyHipergeometricNaiveBayes$clone()

The objects of this class are cloneable with this method.

Usage

DWFuzzyHipergeometricNaiveBayes$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

model <- DWFuzzyHipergeometricNaiveBayesModel$new(wdelta = c(0.5, 0.5), weta = 1)
x <- data.frame(s1 = c(1, 2, 1, 2), n1 = c(5, 5, 6, 6))
y <- factor(c("A", "A", "B", "B"))
model$fit(train = x, cl = y)
model$predict(x)
model

FuzzyClass documentation built on Oct. 22, 2026, 1:07 a.m.