FuzzyClassifier: Base R6 class of the FuzzyClass classifiers

In FuzzyClass: Fuzzy and Non-Fuzzy Classifiers

Base R6 class of the FuzzyClass classifiers

Description

FuzzyClassifier is the (abstract) R6 class shared by every classifier of the package. It implements the common workflow:

• ⁠$new(...)⁠ creates an untrained model holding its hyperparameters;

• ⁠$fit(train, cl)⁠ estimates the model and reports the progress;

• ⁠$predict(newdata, type)⁠ classifies new observations;

• ⁠$set_params(...)⁠ changes hyperparameters (the model must be re-trained).

Do not instantiate it directly: use one of the concrete classes, e.g. FuzzyGaussianNaiveBayesModel, FuzzyGammaNaiveBayesModel or GauNBFuzzyParamModel. Each of them also has a functional interface (e.g. FuzzyGaussianNaiveBayes()) that trains and returns the R6 object, and every classifier is available to tidymodels through fuzzy_naive_bayes().

Since ⁠$fit()⁠ and ⁠$set_params()⁠ return the object invisibly, calls can be chained: FuzzyGammaNaiveBayesModel$new()$fit(x, y)$predict(x_new).

Public fields

params

Named list with the hyperparameters of the classifier.

estimates

List with the quantities estimated by ⁠$fit()⁠ (NULL while the model has not been trained).

predictors

Names of the predictors seen during training.

classes

Classes (levels of the response) seen during training.

n_obs

Number of training observations.

fit_time

Training time, in seconds.

Active bindings

label

Name of the classifier (read-only); it depends on whether the membership functions are used (fuzzy).

Methods

Public methods

• FuzzyClassifier$new()

• FuzzyClassifier$fit()

• FuzzyClassifier$predict()

• FuzzyClassifier$set_params()

• FuzzyClassifier$is_fitted()

• FuzzyClassifier$print()

• FuzzyClassifier$clone()

---

FuzzyClassifier$new()

Create a new (untrained) classifier.

Usage

FuzzyClassifier$new(...)

Arguments

...

Named hyperparameters of the classifier.

---

FuzzyClassifier$fit()

Estimate the classifier from training data.

Usage

FuzzyClassifier$fit(train, cl, verbose = fuzzy_verbose())

Arguments

train

Matrix or data frame of training set cases.

cl

Factor (or vector) of true classifications of the training set.

verbose

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

Returns

The trained object, invisibly.

---

FuzzyClassifier$predict()

Classify new observations.

Usage

FuzzyClassifier$predict(newdata, type = c("class", "prob", "matrix", "raw"))

Arguments

newdata

Matrix or data frame of test set cases, with the predictors in the same order used in training.

type

"class" (default) for a factor of predicted classes, or "prob" for a matrix of class probabilities (one column per class). "matrix" and "raw" are accepted as synonyms of "prob".

Returns

A factor (type = "class") or a numeric matrix.

---

FuzzyClassifier$set_params()

Update hyperparameters. The model is reset and must be trained again with ⁠$fit()⁠.

Usage

FuzzyClassifier$set_params(...)

Arguments

...

Named hyperparameters, as in ⁠$new()⁠.

Returns

The object, invisibly.

---

FuzzyClassifier$is_fitted()

Has the model been trained?

Usage

FuzzyClassifier$is_fitted()

Returns

TRUE or FALSE.

---

FuzzyClassifier$print()

Print a summary of the classifier.

Usage

FuzzyClassifier$print(...)

Arguments

...

Ignored.

---

FuzzyClassifier$clone()

The objects of this class are cloneable with this method.

Usage

FuzzyClassifier$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

model <- FuzzyGaussianNaiveBayesModel$new(fuzzy = TRUE)
model$fit(train = iris[, -5], cl = iris$Species)
model

head(model$predict(iris[, -5]))
head(model$predict(iris[, -5], type = "prob"))

# S3 generics also work
head(predict(model, iris[, -5]))

# Change a hyperparameter and re-train, silently
model$set_params(fuzzy = FALSE)$fit(iris[, -5], iris$Species, verbose = FALSE)
model$label

FuzzyClass documentation built on Oct. 22, 2026, 1:07 a.m.