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Fuzzy Naive Bayes

Source: R/FuzzyNaiveBayes.R
FuzzyNaiveBayes.Rd

FuzzyNaiveBayes Fuzzy Naive Bayes

Usage

FuzzyNaiveBayes(
  train,
  cl,
  fuzzy = TRUE,
  m = NULL,
  Pi = NULL,
  verbose = fuzzy_verbose()
)

Arguments

train

matrix or data frame of training set cases

cl

factor of true classifications of training set

fuzzy

boolean variable to use the membership function

m

is M/N, where M is the number of classes and N is the number of train lines

Pi

is 1/M, where M is the number of classes

verbose

logical; if TRUE (default, see fuzzy_verbose()) a message reports the training progress.

Value

A trained FuzzyNaiveBayesModel 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 (2009). “Another approach for fuzzy naive bayes applied on online training assessment in virtual reality simulators.” In Proceedings of Safety Health and Environmental World Congress, 62–66.

See also

FuzzyNaiveBayesModel for the R6 interface and fuzzy_naive_bayes() (engine "naive") for the tidymodels interface.

Examples


set.seed(1) # determining a seed
data(HouseVotes84)

# Splitting into Training and Testing
split <- caTools::sample.split(t(HouseVotes84[, 1]), SplitRatio = 0.7)
Train <- subset(HouseVotes84, split == "TRUE")
Test <- subset(HouseVotes84, 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[, -1]
fit_FNB <- FuzzyNaiveBayes(
  train = Train[, -1],
  cl = Train[, 1]
)
#> ℹ Fuzzy Naive Bayes: 305 observations, 16 predictors and 2 classes
#> ℹ Training the model: estimating its parameters
#> ✔ Model trained successfully [14ms]
#> 

pred_FNB <- predict(fit_FNB, test)

head(pred_FNB)
#> [1] republican democrat   republican republican republican democrat  
#> Levels: democrat republican
head(Test[, 1])
#> [1] democrat   democrat   democrat   republican republican democrat  
#> Levels: democrat republican