FuzzyBinomialNaiveBayes Fuzzy Binomial Naive Bayes
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
References
Moraes RM, Machado LS (2016). “A Fuzzy Binomial Naive Bayes classifier for epidemiological data.” In 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 745--750. IEEE.
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