Double Weighted Fuzzy Gamma Naive Bayes

DWFuzzyGammaNaiveBayes Double Weighted Fuzzy Gamma Naive Bayes

Usage

DWFuzzyGammaNaiveBayes(
  train,
  cl,
  cores = 2,
  fuzzy = TRUE,
  wdelta,
  weta,
  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

wdelta

vector weight each class

weta

vector weight each feature

verbose

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

Value

A trained DWFuzzyGammaNaiveBayesModel 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, Soares EAMG, Machado LS (2020). “A double weighted fuzzy gamma naive bayes classifier.” Journal of Intelligent & Fuzzy Systems, 38(1), 577–588.

See also

DWFuzzyGammaNaiveBayesModel for the R6 interface and fuzzy_naive_bayes() (engine "dw_gamma") for the tidymodels interface.

Examples

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

# Splitting into Training and Testing
split <- caTools::sample.split(t(GamWeightData[, 1]), SplitRatio = 0.7)
Train <- subset(GamWeightData, split == "TRUE")
Test <- subset(GamWeightData, 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[, -4]
fit_NBT <- DWFuzzyGammaNaiveBayes(
  train = Train[, -4],
  cl = Train[, 4], cores = 2,
  wdelta = c(2.002/6,1.998/6,2.000/6),
  weta = c(3/10,2/10, 5/10)
)
#> ℹ Double Weighted Fuzzy Gamma Naive Bayes: 420 observations, 3 predictors and 3 classes
#> ℹ Training the model: estimating its parameters
#> ✔ Model trained successfully [7ms]
#> 

pred_NBT <- predict(fit_NBT, test)

head(pred_NBT)
#> [1] 1 1 1 1 1 1
#> Levels: 1 2 3
head(Test[, 4])
#> [1] 1 1 1 1 1 1
#> Levels: 1 2 3