Fuzzy Naive Bayes (R6 class)

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

Super class

FuzzyClassifier -> FuzzyNaiveBayes

Methods

Public methods

• FuzzyNaiveBayes$new()

• FuzzyNaiveBayes$clone()

Inherited methods

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

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FuzzyNaiveBayes$new()

Create a new (untrained) classifier.

Usage

FuzzyNaiveBayes$new(fuzzy = TRUE, m = NULL, Pi = NULL)

Arguments

fuzzy

See FuzzyNaiveBayes().

m

See FuzzyNaiveBayes().

Pi

See FuzzyNaiveBayes().

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FuzzyNaiveBayes$clone()

The objects of this class are cloneable with this method.

Usage

FuzzyNaiveBayes$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

model <- FuzzyNaiveBayesModel$new()
model$fit(train = iris[, -5], cl = iris$Species)
#> ℹ Fuzzy Naive Bayes: 150 observations, 4 predictors and 3 classes
#> ℹ Training the model: estimating its parameters
#> ✔ Model trained successfully [6ms]
#> 
head(model$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,] 0.9999970 2.921316e-06 3.824268e-08
#> [2,] 0.9999064 8.004112e-05 1.357392e-05
#> [3,] 0.9997257 2.370988e-04 3.724606e-05
#> [4,] 0.9997994 1.714983e-04 2.908385e-05
#> [5,] 0.9999970 2.921315e-06 7.648535e-08
#> [6,] 0.9616918 3.035953e-02 7.948677e-03
model
#> <FuzzyNaiveBayes> Fuzzy Naive Bayes
#> ✔ Trained on 150 observations in 0.002s
#>   Hyperparameters: fuzzy = TRUE, m = NULL, Pi = NULL
#>   Predictors (4): Sepal.Length, Sepal.Width, Petal.Length, Petal.Width
#>   Classes (3): setosa, versicolor, virginica