Fuzzy Triangular Naive Bayes (R6 class)

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

Super class

FuzzyClassifier -> FuzzyTriangularNaiveBayes

Methods

Public methods

• FuzzyTriangularNaiveBayes$new()

• FuzzyTriangularNaiveBayes$clone()

Inherited methods

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

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FuzzyTriangularNaiveBayes$new()

Create a new (untrained) classifier.

Usage

FuzzyTriangularNaiveBayes$new(fuzzy = TRUE)

Arguments

fuzzy

See FuzzyTriangularNaiveBayes().

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FuzzyTriangularNaiveBayes$clone()

The objects of this class are cloneable with this method.

Usage

FuzzyTriangularNaiveBayes$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

model <- FuzzyTriangularNaiveBayesModel$new()
model$fit(train = iris[, -5], cl = iris$Species)
#> ℹ Fuzzy Triangular Naive Bayes: 150 observations, 4 predictors and 3 classes
#> ℹ Training the model: estimating its parameters
#> ✔ Model trained successfully [6ms]
#> 
head(model$predict(iris[, -5]))
#> [1] setosa setosa setosa setosa setosa setosa
#> Levels: setosa versicolor virginica
head(model$predict(iris[, -5], type = "prob"))
#>      setosa   versicolor    virginica
#> [1,]      1 4.772065e-35 5.627793e-36
#> [2,]      1 3.239345e-24 9.240629e-34
#> [3,]      1 7.466441e-25 1.292511e-33
#> [4,]      1 1.895370e-33 1.248279e-33
#> [5,]      1 4.763587e-35 2.120271e-36
#> [6,]      1 7.612816e-32 2.268199e-33
model
#> <FuzzyTriangularNaiveBayes> Fuzzy Triangular Naive Bayes
#> ✔ Trained on 150 observations in 0.003s
#>   Hyperparameters: fuzzy = TRUE
#>   Predictors (4): Sepal.Length, Sepal.Width, Petal.Length, Petal.Width
#>   Classes (3): setosa, versicolor, virginica