Fuzzy Hipergeometric Naive Bayes

FuzzyHipergeometricNaiveBayes Fuzzy Hipergeometric Naive Bayes

Usage

FuzzyHipergeometricNaiveBayes(
  train,
  cl,
  cores = 2,
  fuzzy = TRUE,
  verbose = fuzzy_verbose()
)

Arguments

train

matrix or data frame of training set cases.

cl

factor of true classifications of training set

cores

kept for backward compatibility; prediction is vectorized and does not start parallel workers (default = 2)

fuzzy

boolean variable to use the membership function

verbose

logical; if TRUE (default, see fuzzy_verbose()) a message reports the training progress.

Value

A trained FuzzyHipergeometricNaiveBayesModel R6 object (which inherits from FuzzyClassifier). Use predict(object, newdata, type = "class") or object$predict(newdata) to classify new observations; type = "prob" (or "matrix") returns the matrix of class probabilities.

References

Ferreira J, Machado LS, de Moraes RM (2025). “A New Double Weighted Fuzzy Hypergeometric Naive Bayes Network and its Application for User’s Assessment in Virtual Reality Simulators.” International Journal of Computational Intelligence Systems, 18(1), 1–17.

See also

FuzzyHipergeometricNaiveBayesModel for the R6 interface and fuzzy_naive_bayes() (engine "hypergeometric") for the tidymodels interface.

Examples

set.seed(1) # determining a seed

substituir_zero <- function(x) {
  if (x == 0) {
    while(1){
     new_valor <- rhyper(1,3,30,10)
      if(new_valor != 0){
        return(new_valor)
      }
    }
  } else {
    return(x)
  }
}

#Building dataframe
class1 <- data.frame(rhyper(72,3,30,10), 
                     sample(seq(1,5),72,replace = TRUE), class = "Bem treinado")
colnames(class1)[1] <- "sucessos"
class1$sucessos <- sapply(class1$sucessos, substituir_zero)
colnames(class1)[2] <- "tentativas"
colnames(class1)[3] <- "avaliação"
class2 <- data.frame(rhyper(72,3,17,12), 
                     sample(seq(4,7),72,replace = TRUE), class = "Mediano")
colnames(class2)[1] <- "sucessos"
colnames(class2)[2] <- "tentativas"
colnames(class2)[3] <- "avaliação"
class3 <- data.frame(rhyper(72,3,9,11),
                     sample(seq(7,10),72,replace = TRUE), class = "Precisa treinar")
colnames(class3)[1] <- "sucessos"
colnames(class3)[2] <- "tentativas"
colnames(class3)[3] <- "avaliação"
data <- rbind(class1,class2,class3)


#spliting dataframe
split <- caTools::sample.split(t(data[, 1]), SplitRatio = 0.7)
Train <- subset(data, split == "TRUE")
Test <- subset(data, split == "FALSE")

test <- Test[, -3]
fit_NHT <- FuzzyHipergeometricNaiveBayes(
 train = Train[, -3],
 cl = Train[, 3], cores = 2
)
#> ℹ Fuzzy Hypergeometric Naive Bayes: 151 observations, 2 predictors and 3 classes
#> ℹ Training the model: estimating its parameters
#> ✔ Model trained successfully [5ms]
#> 

pred_NHT <- predict(fit_NHT, test)