man/rmd/naive_Bayes_h2o.md

In parsnip: A Common API to Modeling and Analysis Functions

For this engine, there is a single mode: classification

Tuning Parameters

This model has 1 tuning parameter:

• Laplace: Laplace Correction (type: double, default: 0.0)

[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

The agua extension package is required to fit this model.

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

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

## Naive Bayes Model Specification (classification)
## 
## Main Arguments:
##   Laplace = numeric(0)
## 
## Computational engine: h2o 
## 
## Model fit template:
## agua::h2o_train_nb(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
##     validation_frame = missing_arg(), laplace = numeric(0))

Initializing h2o

To use the h2o engine with tidymodels, please run h2o::h2o.init() first. By default, This connects R to the local h2o server. This needs to be done in every new R session. You can also connect to a remote h2o server with an IP address, for more details see [h2o::h2o.init()].

You can control the number of threads in the thread pool used by h2o with the nthreads argument. By default, it uses all CPUs on the host. This is different from the usual parallel processing mechanism in tidymodels for tuning, while tidymodels parallelizes over resamples, h2o parallelizes over hyperparameter combinations for a given resample.

h2o will automatically shut down the local h2o instance started by R when R is terminated. To manually stop the h2o server, run h2o::h2o.shutdown().

Saving fitted model objects

Models fitted with this engine may require native serialization methods to be properly saved and/or passed between R sessions. To learn more about preparing fitted models for serialization, see the bundle package.

parsnip documentation built on Aug. 18, 2023, 1:07 a.m.