Fuzzy Binomial Naive Bayes (R6 class)

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

Super class

FuzzyClassifier -> FuzzyBinomialNaiveBayes

Methods

Public methods

• FuzzyBinomialNaiveBayes$new()

• FuzzyBinomialNaiveBayes$clone()

Inherited methods

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

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FuzzyBinomialNaiveBayes$new()

Create a new (untrained) classifier.

Usage

FuzzyBinomialNaiveBayes$new(fuzzy = TRUE)

Arguments

fuzzy

See FuzzyBinomialNaiveBayes().

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FuzzyBinomialNaiveBayes$clone()

The objects of this class are cloneable with this method.

Usage

FuzzyBinomialNaiveBayes$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

model <- FuzzyBinomialNaiveBayesModel$new()
x <- round(iris[, -5])
model$fit(train = x, cl = iris$Species)
#> ℹ Fuzzy Binomial Naive Bayes: 150 observations, 4 predictors and 3 classes
#> ℹ Training the model: estimating its parameters
#> ✔ Model trained successfully [5ms]
#> 
head(model$predict(x))
#> [1] setosa setosa setosa setosa setosa setosa
#> Levels: setosa versicolor virginica
head(model$predict(x, type = "prob"))
#>         setosa   versicolor    virginica
#> [1,] 1.0000000 0.000000e+00 4.642555e-14
#> [2,] 0.9999845 1.552739e-05 1.097178e-12
#> [3,] 0.9999845 1.552739e-05 1.097178e-12
#> [4,] 0.9999021 9.794360e-05 7.814212e-12
#> [5,] 1.0000000 0.000000e+00 4.642555e-14
#> [6,] 1.0000000 0.000000e+00 3.306748e-13
model
#> <FuzzyBinomialNaiveBayes> Fuzzy Binomial 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