Poisson Naive Bayes with Fuzzy Parameters (R6 class)

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

Super class

FuzzyClassifier -> PoiNBFuzzyParam

Methods

Public methods

• PoiNBFuzzyParam$new()

• PoiNBFuzzyParam$clone()

Inherited methods

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

---

PoiNBFuzzyParam$new()

Create a new (untrained) classifier.

Usage

PoiNBFuzzyParam$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 PoiNBFuzzyParam().

metd

See PoiNBFuzzyParam().

alp

See PoiNBFuzzyParam().

w

See PoiNBFuzzyParam().

---

PoiNBFuzzyParam$clone()

The objects of this class are cloneable with this method.

Usage

PoiNBFuzzyParam$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

model <- PoiNBFuzzyParamModel$new()
x <- round(iris[, -5])
model$fit(train = x, cl = iris$Species)
#> ℹ Poisson Naive Bayes with Fuzzy Parameters: 150 observations, 4 predictors and 3 classes
#> ℹ Training the model: estimating its parameters
#> ✔ Model trained successfully [5ms]
#> 
head(model$predict(x))
#> [1] setosa setosa setosa setosa setosa setosa
#> Levels: setosa versicolor virginica
head(model$predict(x, type = "prob"))
#>         setosa versicolor   virginica
#> [1,] 0.9714948 0.02235177 0.006153474
#> [2,] 0.9606296 0.03048538 0.008885010
#> [3,] 0.9606296 0.03048538 0.008885010
#> [4,] 0.9343234 0.05187582 0.013800742
#> [5,] 0.9714948 0.02235177 0.006153474
#> [6,] 0.9502309 0.03980497 0.009964100
model
#> <PoiNBFuzzyParam> Poisson 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