Fuzzy Bayes Rule

FuzzyBayesRule Fuzzy Bayes Rule

Usage

FuzzyBayesRule(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 FuzzyBayesRuleModel 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 R, Machado L (2008). “Fuzzy Bayes Rule for On-Line Training Assessment in Virtual Reality Simulators.” Multiple-Valued Logic and Soft Computing, 14, 325-338.

See also

FuzzyBayesRuleModel for the R6 interface and fuzzy_naive_bayes() (engine "bayes_rule") 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 <- FuzzyBayesRule(
  train = Train[, -5],
  cl = Train[, 5], cores = 2
)
#> ℹ Fuzzy Bayes Rule: 105 observations, 4 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] setosa setosa setosa setosa setosa setosa
#> Levels: setosa versicolor virginica
head(Test[, 5])
#> [1] setosa setosa setosa setosa setosa setosa
#> Levels: setosa versicolor virginica