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Fuzzy Beta Naive Bayes

Source: R/FuzzyBetaNaiveBayes.R
FuzzyBetaNaiveBayes.Rd

FuzzyBetaNaiveBayes Fuzzy Beta Naive Bayes

Usage

FuzzyBetaNaiveBayes(
  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 FuzzyBetaNaiveBayesModel 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, Rodrigues AKG, Soares EAMG, Machado LS (2020). “A new fuzzy beta naive Bayes classifier.” In Developments of Artificial Intelligence Technologies in Computation and Robotics: Proceedings of the 14th International FLINS Conference (FLINS 2020), 437–445. World Scientific.

See also

FuzzyBetaNaiveBayesModel for the R6 interface and fuzzy_naive_bayes() (engine "beta") 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 <- FuzzyBetaNaiveBayes(
  train = Train[, -5],
  cl = Train[, 5], cores = 2
)
#> ℹ Fuzzy Beta Naive Bayes: 105 observations, 4 predictors and 3 classes
#> ℹ Training the model: estimating its parameters
#> ✔ Model trained successfully [13ms]
#> 

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