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.

There are different ways to fit this model, and the method of estimation is chosen by setting the model engine. The engine-specific pages for this model are listed below.

• klaR¹²

• h2o²

• naivebayes²

¹ The default engine. ² Requires a parsnip extension package.

More information on how parsnip is used for modeling is at https://www.tidymodels.org/.

Usage

naive_Bayes(
  mode = "classification",
  smoothness = NULL,
  Laplace = NULL,
  engine = "klaR"
)

Arguments

mode

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

smoothness

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

Laplace

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

engine

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

Details

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

https://www.tidymodels.org, Tidy Modeling with R, searchable table of parsnip models

See also

fit(), set_engine(), update(), klaR engine details, h2o engine details, naivebayes engine details