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

Super class

FuzzyClassifier -> FuzzyExponentialNaiveBayes

Methods

Inherited methods


FuzzyExponentialNaiveBayes$new()

Create a new (untrained) classifier.

Usage

FuzzyExponentialNaiveBayes$new(fuzzy = TRUE)

Arguments


FuzzyExponentialNaiveBayes$clone()

The objects of this class are cloneable with this method.

Usage

FuzzyExponentialNaiveBayes$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

model <- FuzzyExponentialNaiveBayesModel$new()
model$fit(train = iris[, -5], cl = iris$Species)
#> ℹ Fuzzy Exponential Naive Bayes: 150 observations, 4 predictors and 3 classes
#> ℹ Training the model: estimating its parameters
#> ✔ Model trained successfully [5ms]
#> 
head(model$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,] 0.9997578 0.000230420 1.180106e-05
#> [2,] 0.9983755 0.001346715 2.777607e-04
#> [3,] 0.9973642 0.002194846 4.409301e-04
#> [4,] 0.9974935 0.002076859 4.296132e-04
#> [5,] 0.9997543 0.000228113 1.753854e-05
#> [6,] 0.9238276 0.057099756 1.907260e-02
model
#> <FuzzyExponentialNaiveBayes> Fuzzy Exponential Naive Bayes
#> ✔ Trained on 150 observations in 0.002s
#>   Hyperparameters: fuzzy = TRUE
#>   Predictors (4): Sepal.Length, Sepal.Width, Petal.Length, Petal.Width
#>   Classes (3): setosa, versicolor, virginica