Fuzzy Hypergeometric Naive Bayes (R6 class)

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

Super class

FuzzyClassifier -> FuzzyHipergeometricNaiveBayes

Methods

Public methods

• FuzzyHipergeometricNaiveBayes$new()

• FuzzyHipergeometricNaiveBayes$clone()

Inherited methods

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

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FuzzyHipergeometricNaiveBayes$new()

Create a new (untrained) classifier.

Usage

FuzzyHipergeometricNaiveBayes$new(fuzzy = TRUE)

Arguments

fuzzy

See FuzzyHipergeometricNaiveBayes().

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FuzzyHipergeometricNaiveBayes$clone()

The objects of this class are cloneable with this method.

Usage

FuzzyHipergeometricNaiveBayes$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

model <- FuzzyHipergeometricNaiveBayesModel$new()
x <- data.frame(s1 = c(1, 2, 1, 2), n1 = c(5, 5, 6, 6))
y <- factor(c("A", "A", "B", "B"))
model$fit(train = x, cl = y)
#> ℹ Fuzzy Hypergeometric Naive Bayes: 4 observations, 2 predictors and 2 classes
#> ℹ Training the model: estimating its parameters
#> ✔ Model trained successfully [10ms]
#> 
model$predict(x)
#> [1] A B A B
#> Levels: A B
model
#> <FuzzyHipergeometricNaiveBayes> Fuzzy Hypergeometric Naive Bayes
#> ✔ Trained on 4 observations in 0.001s
#>   Hyperparameters: fuzzy = TRUE
#>   Predictors (2): s1, n1
#>   Classes (2): A, B