In parsnip: A Common API to Modeling and Analysis Functions

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r descr_models("naive_Bayes", "h2o")

Tuning Parameters

#| label: h2o-param-info
#| echo: false
defaults <- 
  tibble::tibble(parsnip = c("Laplace"),
                 default = c("0.0"))

param <-
  naive_Bayes() |> 
  set_engine("h2o") |> 
  make_parameter_list(defaults)

This model has r nrow(param) tuning parameter:

#| label: h2o-param-list
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#| results: asis
param$item

[h2o::h2o.naiveBayes()] provides several engine arguments to deal with imbalances and rare classes:

• balance_classes A logical value controlling over/under-sampling (for imbalanced data). Defaults to FALSE.

• class_sampling_factors The over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Require balance_classes to be TRUE.

• min_sdev: The minimum standard deviation to use for observations without enough data, must be greater than 1e-10.

• min_prob: The minimum probability to use for observations with not enough data.

Translation from parsnip to the original package

r uses_extension("naive_Bayes", "h2o", "classification")

[agua::h2o_train_nb()] is a wrapper around [h2o::h2o.naiveBayes()].

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naive_Bayes(Laplace = numeric(0)) |> 
  set_engine("h2o") |> 
  translate()

Initializing h2o

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Saving fitted model objects

#| child: template-bundle.Rmd

parsnip documentation built on June 8, 2025, 12:10 p.m.