In topepo/parsnip: A Common API to Modeling and Analysis Functions

r descr_models("naive_Bayes", "klaR")

Tuning Parameters

defaults <- 
  tibble::tibble(parsnip = c("smoothness", "Laplace"),
                 default = c("1.0", "0.0"))

param <-
  naive_Bayes() %>% 
  set_engine("klaR") %>% 
  make_parameter_list(defaults)

This model has r nrow(param) tuning parameter:

param$item

Note that the engine argument usekernel is set to TRUE by default when using the klaR engine.

Translation from parsnip to the original package

r uses_extension("naive_Bayes", "klaR", "classification")

library(discrim)

naive_Bayes(smoothness = numeric(0), Laplace = numeric(0)) %>% 
  set_engine("klaR") %>% 
  translate()

Preprocessing requirements

The columns for qualitative predictors should always be represented as factors (as opposed to dummy/indicator variables). When the predictors are factors, the underlying code treats them as multinomial data and appropriately computes their conditional distributions.