Fuzzy Gaussian Naive Bayes (Zadeh-based) (R6 class)
Source:R/FuzzyGaussianNaiveBayes.R
FuzzyGaussianNaiveBayesModel.RdR6 implementation of the Fuzzy Gaussian Naive Bayes (Zadeh-based) classifier. Create the model with
FuzzyGaussianNaiveBayesModel$new(), train it with $fit(train, cl) and classify new
observations with $predict(newdata). The functional interface
FuzzyGaussianNaiveBayes() trains and returns an object of this class.
Super class
FuzzyClassifier -> FuzzyGaussianNaiveBayes
Methods
FuzzyGaussianNaiveBayes$new()
Create a new (untrained) classifier.
Usage
FuzzyGaussianNaiveBayes$new(fuzzy = TRUE)Examples
model <- FuzzyGaussianNaiveBayesModel$new()
model$fit(train = iris[, -5], cl = iris$Species)
#> ℹ Fuzzy Gaussian Naive Bayes (Zadeh-based): 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,] 1 3.089056e-21 2.007145e-29
#> [2,] 1 1.868147e-19 1.505721e-27
#> [3,] 1 2.429303e-20 2.675230e-28
#> [4,] 1 2.714064e-19 2.829119e-27
#> [5,] 1 1.054105e-21 1.242936e-29
#> [6,] 1 3.466495e-15 2.992237e-22
model
#> <FuzzyGaussianNaiveBayes> Fuzzy Gaussian Naive Bayes (Zadeh-based)
#> ✔ 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