Double Weighted Fuzzy Hypergeometric Naive Bayes (R6 class)

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)
#> ℹ Double Weighted Fuzzy Hypergeometric Naive Bayes: 4 observations, 2 predictors and 2 classes
#> ℹ Training the model: estimating its parameters
#> ✔ Model trained successfully [4ms]
#> 
model$predict(x)
#> [1] A B A B
#> Levels: A B
model
#> <DWFuzzyHipergeometricNaiveBayes> Double Weighted Fuzzy Hypergeometric Naive Bayes
#> ✔ Trained on 4 observations in 0.001s
#>   Hyperparameters: fuzzy = TRUE, wdelta = c(0.5, 0.5), weta = 1
#>   Predictors (2): s1, n1
#>   Classes (2): A, B