Fuzzy Naive Bayes Trapezoidal Classifier

FuzzyTrapezoidalNaiveBayes Fuzzy Naive Bayes Trapezoidal Classifier

Usage

FuzzyTrapezoidalNaiveBayes(
  train,
  cl,
  cores = 2,
  fuzzy = T,
  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 FuzzyTrapezoidalNaiveBayesModel 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

Lopes A, Ferreira J, Machado LS, Moraes RM (2022). “A New Fuzzy Trapezoidal Naive Bayes Network as basis for Assessment in Training based on Virtual Reality.” In The 15th International FLINS Conference on Machine learning, Multi agent and Cyber physical systems (FLINS 2022). Nankai University.

See also

FuzzyTrapezoidalNaiveBayesModel for the R6 interface and fuzzy_naive_bayes() (engine "trapezoidal") 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 <- FuzzyTrapezoidalNaiveBayes(
  train = Train[, -5],
  cl = Train[, 5], cores = 2
)
#> ℹ Fuzzy Trapezoidal Naive Bayes: 105 observations, 4 predictors and 3 classes
#> ℹ Training the model: estimating its parameters
#> ✔ Model trained successfully [11ms]
#> 

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