Exponential Naive Bayes with Fuzzy Parameters (R6 class)

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

Super class

FuzzyClassifier -> ExpNBFuzzyParam

Methods

Public methods

• ExpNBFuzzyParam$new()

• ExpNBFuzzyParam$clone()

Inherited methods

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

---

ExpNBFuzzyParam$new()

Create a new (untrained) classifier.

Usage

ExpNBFuzzyParam$new(
  alphacut = 1e-04,
  metd = 2,
  alp = c(0.35, 0.7, 0.86),
  w = c(0.1, 0.3, 0.6)
)

Arguments

alphacut

See ExpNBFuzzyParam().

metd

See ExpNBFuzzyParam().

alp

See ExpNBFuzzyParam().

w

See ExpNBFuzzyParam().

---

ExpNBFuzzyParam$clone()

The objects of this class are cloneable with this method.

Usage

ExpNBFuzzyParam$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

model <- ExpNBFuzzyParamModel$new()
model$fit(train = iris[, -5], cl = iris$Species)
#> ℹ Exponential Naive Bayes with Fuzzy Parameters: 150 observations, 4 predictors and 3 classes
#> ℹ Training the model: estimating its parameters
#> ✔ Model trained successfully [5ms]
#> 
head(model$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,] 0.9276884 0.05351419 0.01879743
#> [2,] 0.9246951 0.05533441 0.01997048
#> [3,] 0.9267533 0.05399649 0.01925021
#> [4,] 0.9239140 0.05588354 0.02020246
#> [5,] 0.9278839 0.05339115 0.01872497
#> [6,] 0.6432215 0.24967286 0.10710568
model
#> <ExpNBFuzzyParam> Exponential Naive Bayes with Fuzzy Parameters
#> ✔ Trained on 150 observations in 0.001s
#>   Hyperparameters: alphacut = 1e-04, metd = 2, alp = c(0.35, 0.7, 0.86), w = c(0.1, 0.3, 0.6)
#>   Predictors (4): Sepal.Length, Sepal.Width, Petal.Length, Petal.Width
#>   Classes (3): setosa, versicolor, virginica