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Fuzzy Exponential Naive Bayes

Source: R/FuzzyExponentialNaiveBayes.R
FuzzyExponentialNaiveBayes.Rd

FuzzyExponentialNaiveBayes Fuzzy Exponential Naive Bayes

Usage

FuzzyExponentialNaiveBayes(
  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 FuzzyExponentialNaiveBayesModel 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

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.

See also

FuzzyExponentialNaiveBayesModel for the R6 interface and fuzzy_naive_bayes() (engine "exponential") for the tidymodels interface.

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
)
#> ℹ Fuzzy Exponential Naive Bayes: 105 observations, 4 predictors and 3 classes
#> ℹ Training the model: estimating its parameters
#> ✔ Model trained successfully [5ms]
#> 

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