Fuzzy Bayes Rule (R6 class)

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

Super class

FuzzyClassifier -> FuzzyBayesRule

Methods

Public methods

• FuzzyBayesRule$new()

• FuzzyBayesRule$clone()

Inherited methods

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

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FuzzyBayesRule$new()

Create a new (untrained) classifier.

Usage

FuzzyBayesRule$new(fuzzy = TRUE)

Arguments

fuzzy

See FuzzyBayesRule().

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FuzzyBayesRule$clone()

The objects of this class are cloneable with this method.

Usage

FuzzyBayesRule$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

model <- FuzzyBayesRuleModel$new()
model$fit(train = iris[, -5], cl = iris$Species)
#> ℹ Fuzzy Bayes Rule: 150 observations, 4 predictors and 3 classes
#> ℹ Training the model: estimating its parameters
#> ✔ Model trained successfully [5ms]
#> 
head(model$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,]      1 5.096275e-29 2.780366e-45
#> [2,]      1 4.512707e-21 2.845258e-37
#> [3,]      1 1.593062e-23 1.248931e-38
#> [4,]      1 7.338988e-21 2.780687e-34
#> [5,]      1 3.487249e-30 2.811773e-45
#> [6,]      1 1.878231e-27 8.760727e-42
model
#> <FuzzyBayesRule> Fuzzy Bayes Rule
#> ✔ 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