Gaussian Naive Bayes with Fuzzy Parameters (R6 class)

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

Super class

FuzzyClassifier -> GauNBFuzzyParam

Methods

Public methods

• GauNBFuzzyParam$new()

• GauNBFuzzyParam$clone()

Inherited methods

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

---

GauNBFuzzyParam$new()

Create a new (untrained) classifier.

Usage

GauNBFuzzyParam$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 GauNBFuzzyParam().

metd

See GauNBFuzzyParam().

alp

See GauNBFuzzyParam().

w

See GauNBFuzzyParam().

---

GauNBFuzzyParam$clone()

The objects of this class are cloneable with this method.

Usage

GauNBFuzzyParam$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

model <- GauNBFuzzyParamModel$new()
model$fit(train = iris[, -5], cl = iris$Species)
#> ℹ Gaussian Naive Bayes with Fuzzy Parameters: 150 observations, 4 predictors and 3 classes
#> ℹ Training the model: estimating its parameters
#> ✔ Model trained successfully [6ms]
#> 
head(model$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,] 0.9999635 1.809295e-05 1.838935e-05
#> [2,] 0.9991539 3.173806e-04 5.286910e-04
#> [3,] 0.9997328 5.620449e-05 2.110275e-04
#> [4,] 0.9994605 1.264808e-04 4.130517e-04
#> [5,] 0.9999905 4.140095e-06 5.332975e-06
#> [6,] 0.9993921 5.885719e-04 1.929828e-05
model
#> <GauNBFuzzyParam> Gaussian Naive Bayes with Fuzzy Parameters
#> ✔ Trained on 150 observations in 0.002s
#>   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