Fuzzy Beta Naive Bayes (R6 class)

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

Super class

FuzzyClassifier -> FuzzyBetaNaiveBayes

Methods

Public methods

• FuzzyBetaNaiveBayes$new()

• FuzzyBetaNaiveBayes$clone()

Inherited methods

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

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FuzzyBetaNaiveBayes$new()

Create a new (untrained) classifier.

Usage

FuzzyBetaNaiveBayes$new(fuzzy = TRUE)

Arguments

fuzzy

See FuzzyBetaNaiveBayes().

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FuzzyBetaNaiveBayes$clone()

The objects of this class are cloneable with this method.

Usage

FuzzyBetaNaiveBayes$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

model <- FuzzyBetaNaiveBayesModel$new()
model$fit(train = iris[, -5] / 10, cl = iris$Species)
#> ℹ Fuzzy Beta Naive Bayes: 150 observations, 4 predictors and 3 classes
#> ℹ Training the model: estimating its parameters
#> ✔ Model trained successfully [12ms]
#> 
head(model$predict(iris[, -5] / 10))
#> [1] setosa setosa setosa setosa setosa setosa
#> Levels: setosa versicolor virginica
head(model$predict(iris[, -5] / 10, type = "prob"))
#>      setosa   versicolor    virginica
#> [1,]      1 6.379672e-27 3.805856e-22
#> [2,]      1 1.006187e-24 6.258096e-20
#> [3,]      1 5.427808e-26 2.081452e-20
#> [4,]      1 3.865326e-24 1.801236e-19
#> [5,]      1 5.910458e-27 9.044085e-22
#> [6,]      1 4.222896e-18 1.204009e-15
model
#> <FuzzyBetaNaiveBayes> Fuzzy Beta Naive Bayes
#> ✔ Trained on 150 observations in 0.009s
#>   Hyperparameters: fuzzy = TRUE
#>   Predictors (4): Sepal.Length, Sepal.Width, Petal.Length, Petal.Width
#>   Classes (3): setosa, versicolor, virginica