FuzzyExponentialNaiveBayes
Fuzzy Exponential 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 exponential naive bayes classifier.” In Uncertainty Modelling in Knowledge Engineering and Decision Making: Proceedings of the 12th International FLINS Conference, 207--212. World Scientific.
Examples
set.seed(1) # determining a seed
data(iris)
# Splitting into Training and Testing
split <- caTools::sample.split(t(iris[, 1]), SplitRatio = 0.7)
Train <- subset(iris, split == "TRUE")
Test <- subset(iris, 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[, -5]
fit_NBT <- FuzzyExponentialNaiveBayes(
train = Train[, -5],
cl = Train[, 5], cores = 2
)
pred_NBT <- predict(fit_NBT, test)
head(pred_NBT)
#> [1] setosa setosa setosa setosa setosa setosa
#> Levels: setosa versicolor virginica
head(Test[, 5])
#> [1] setosa setosa setosa setosa setosa setosa
#> Levels: setosa versicolor virginica