Skip to contents

Fuzzy Poisson Naive Bayes

Source: R/FuzzyPoissonNaiveBayes.R
FuzzyPoissonNaiveBayes.Rd

FuzzyPoissonNaiveBayes Fuzzy Poisson Naive Bayes

Usage

FuzzyPoissonNaiveBayes(
  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 FuzzyPoissonNaiveBayesModel 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 (2015). “A fuzzy poisson naive bayes classifier for epidemiological purposes.” In 2015 7th International Joint Conference on Computational Intelligence (IJCCI), volume 2, 193–198. IEEE.

See also

FuzzyPoissonNaiveBayesModel for the R6 interface and fuzzy_naive_bayes() (engine "poisson") for the tidymodels interface.

Examples


set.seed(1) # determining a seed
class1 <- data.frame(vari1 = rpois(100,lambda = 2),
                     vari2 = rpois(100,lambda = 2),
                     vari3 = rpois(100,lambda = 2), class = 1)
class2 <- data.frame(vari1 = rpois(100,lambda = 1),
                     vari2 = rpois(100,lambda = 1),
                     vari3 = rpois(100,lambda = 1), class = 2)
class3 <- data.frame(vari1 = rpois(100,lambda = 5),
                     vari2 = rpois(100,lambda = 5),
                     vari3 = rpois(100,lambda = 5), class = 3)
data <- rbind(class1,class2,class3)

# Splitting into Training and Testing
split <- caTools::sample.split(t(data[, 1]), SplitRatio = 0.7)
Train <- subset(data, split == "TRUE")
Test <- subset(data, 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 <- FuzzyPoissonNaiveBayes(
  train = Train[, -4],
  cl = Train[, 4], cores = 2
)
#> ℹ Fuzzy Poisson Naive Bayes: 210 observations, 3 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] 2 2 2 1 1 1
#> Levels: 1 2 3
head(Test[, 4])
#> [1] 1 1 1 1 1 1