FuzzyBinomialNaiveBayes: Fuzzy Binomial Naive Bayes

In FuzzyClass: Fuzzy and Non-Fuzzy Classifiers

Fuzzy Binomial Naive Bayes

Description

FuzzyBinomialNaiveBayes Fuzzy Binomial Naive Bayes

Usage

FuzzyBinomialNaiveBayes(train, cl, cores = 2, fuzzy = TRUE)

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

Value

A vector of classifications

References

\insertRef

moraes2016fuzzybinomFuzzyClass

Examples

set.seed(1) # determining a seed
class1 <- data.frame(vari1 = rbinom(100,size = 10, prob = 0.2),
                     vari2 = rbinom(100,size = 10, prob = 0.2),
                    vari3 = rbinom(100,size = 10, prob = 0.2), class = 1)
class2 <- data.frame(vari1 = rbinom(100,size = 10, prob = 0.5),
                     vari2 = rbinom(100,size = 10, prob = 0.5),
                    vari3 = rbinom(100,size = 10, prob = 0.5), class = 2)
class3 <- data.frame(vari1 = rbinom(100,size = 10, prob = 0.8),
                     vari2 = rbinom(100,size = 10, prob = 0.8),
                     vari3 = rbinom(100,size = 10, prob = 0.8), class = 3)
data <- rbind(class1,class2,class3)

# Splitting into Training and Testing
split <- caTools::sample.split(t(data[, 1]), SplitRatio = 0.7)
Train <- subset(data, split == "TRUE")
Test <- subset(data, split == "FALSE")
# ----------------
# matrix or data frame of test set cases.
# A vector will be interpreted as a row vector for a single case.
test <- Test[, -4]
fit_NBT <- FuzzyBinomialNaiveBayes(
  train = Train[, -4],
  cl = Train[, 4], cores = 2
)

pred_NBT <- predict(fit_NBT, test)

head(pred_NBT)
head(Test[, 4])

FuzzyClass documentation built on Oct. 9, 2026, 5:07 p.m.