Fuzzy Trapezoidal Naive Bayes (R6 class)

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

Super class

FuzzyClassifier -> FuzzyTrapezoidalNaiveBayes

Methods

Public methods

• FuzzyTrapezoidalNaiveBayes$new()

• FuzzyTrapezoidalNaiveBayes$clone()

Inherited methods

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

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FuzzyTrapezoidalNaiveBayes$new()

Create a new (untrained) classifier.

Usage

FuzzyTrapezoidalNaiveBayes$new(fuzzy = T)

Arguments

fuzzy

See FuzzyTrapezoidalNaiveBayes().

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FuzzyTrapezoidalNaiveBayes$clone()

The objects of this class are cloneable with this method.

Usage

FuzzyTrapezoidalNaiveBayes$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

model <- FuzzyTrapezoidalNaiveBayesModel$new()
model$fit(train = iris[, -5], cl = iris$Species)
#> ℹ Fuzzy Trapezoidal Naive Bayes: 150 observations, 4 predictors and 3 classes
#> ℹ Training the model: estimating its parameters
#> ✔ Model trained successfully [9ms]
#> 
head(model$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 5.110629e-34 4.063011e-26
#> [2,]      1 5.258433e-33 1.760757e-33
#> [3,]      1 1.220708e-32 3.996640e-33
#> [4,]      1 6.310119e-33 2.112909e-33
#> [5,]      1 2.870785e-34 2.319120e-26
#> [6,]      1 8.909196e-32 1.637972e-32
model
#> <FuzzyTrapezoidalNaiveBayes> Fuzzy Trapezoidal Naive Bayes
#> ✔ Trained on 150 observations in 0.005s
#>   Hyperparameters: fuzzy = TRUE
#>   Predictors (4): Sepal.Length, Sepal.Width, Petal.Length, Petal.Width
#>   Classes (3): setosa, versicolor, virginica