In parsnip: A Common API to Modeling and Analysis Functions

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r descr_models("naive_Bayes", "klaR")

Tuning Parameters

#| label: klaR-param-info
#| echo: false
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:

#| label: klaR-param-list
#| echo: false
#| results: asis
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")

#| label: klaR-cls
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.

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Case weights

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Prediction types

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parsnip:::get_from_env("naive_Bayes_predict") |>
  dplyr::filter(engine == "klaR") |>
  dplyr::select(mode, type)

References

• Kuhn, M, and K Johnson. 2013. Applied Predictive Modeling. Springer.

parsnip documentation built on Jan. 11, 2026, 9:06 a.m.