Fuzzy Binomial Naive Bayes

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

how many cores of the computer do you want to use to use for prediction (default = 2)

fuzzy

boolean variable to use the membership function

Value

A vector of classifications

References

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)
#> [1] 1 1 1 1 1 1
#> Levels: 1 2 3
head(Test[, 4])
#> [1] 1 1 1 1 1 1