Fuzzy Geometric Naive Bayes (R6 class)

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

Super class

FuzzyClassifier -> FuzzyGeoNaiveBayes

Methods

Public methods

• FuzzyGeoNaiveBayes$new()

• FuzzyGeoNaiveBayes$clone()

Inherited methods

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

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FuzzyGeoNaiveBayes$new()

Create a new (untrained) classifier.

Usage

FuzzyGeoNaiveBayes$new(fuzzy = T)

Arguments

fuzzy

See FuzzyGeoNaiveBayes().

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FuzzyGeoNaiveBayes$clone()

The objects of this class are cloneable with this method.

Usage

FuzzyGeoNaiveBayes$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

model <- FuzzyGeoNaiveBayesModel$new()
x <- round(iris[, -5])
model$fit(train = x, cl = iris$Species)
#> ℹ Fuzzy Geometric Naive Bayes: 150 observations, 4 predictors and 3 classes
#> ℹ Training the model: estimating its parameters
#> ✔ Model trained successfully [5ms]
#> 
head(model$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 5.954024e-11 6.095216e-10
#> [2,] 0.9999999 5.123826e-08 7.197528e-09
#> [3,] 0.9999999 5.123826e-08 7.197528e-09
#> [4,] 0.9999999 6.339152e-08 9.350967e-09
#> [5,] 1.0000000 5.954024e-11 6.095216e-10
#> [6,] 1.0000000 7.366266e-11 7.918853e-10
model
#> <FuzzyGeoNaiveBayes> Fuzzy Geometric Naive Bayes
#> ✔ Trained on 150 observations in 0.001s
#>   Hyperparameters: fuzzy = TRUE
#>   Predictors (4): Sepal.Length, Sepal.Width, Petal.Length, Petal.Width
#>   Classes (3): setosa, versicolor, virginica