naive_Bayes: Naive Bayes models

View source: R/naive_Bayes.R

naive_BayesR Documentation

Naive Bayes models


naive_Bayes() defines a model that uses Bayes' theorem to compute the probability of each class, given the predictor values. This function can fit classification models.


More information on how parsnip is used for modeling is at


  mode = "classification",
  smoothness = NULL,
  Laplace = NULL,
  engine = "klaR"



A single character string for the prediction outcome mode. Possible values for this model are "unknown", "regression", or "classification".


An non-negative number representing the the relative smoothness of the class boundary. Smaller examples result in model flexible boundaries and larger values generate class boundaries that are less adaptable


A non-negative value for the Laplace correction to smoothing low-frequency counts.


A single character string specifying what computational engine to use for fitting.


This function only defines what type of model is being fit. Once an engine is specified, the method to fit the model is also defined. See set_engine() for more on setting the engine, including how to set engine arguments.

The model is not trained or fit until the fit() function is used with the data.

Each of the arguments in this function other than mode and engine are captured as quosures. To pass values programmatically, use the injection operator like so:

value <- 1
naive_Bayes(argument = !!value)

References, Tidy Modeling with R, searchable table of parsnip models

See Also


parsnip documentation built on June 24, 2024, 5:14 p.m.