Naive Bayes modelsSource:
naive_Bayes() defines a model that uses Bayes' theorem to compute the
probability of each class, given the predictor values. This function can fit
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.
More information on how parsnip is used for modeling is at https://www.tidymodels.org/.
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
The model is not trained or fit until the
fit() function is used
with the data.
value <- 1 naive_Bayes(argument = !!value)