details_naive_Bayes_naivebayes: Naive Bayes models via naivebayes

details_naive_Bayes_naivebayesR Documentation

Naive Bayes models via naivebayes


naivebayes::naive_bayes() fits a model that uses Bayes' theorem to compute the probability of each class, given the predictor values.


For this engine, there is a single mode: classification

Tuning Parameters

This model has 2 tuning parameter:

  • smoothness: Kernel Smoothness (type: double, default: 1.0)

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

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

Translation from parsnip to the original package

The discrim extension package is required to fit this model.


naive_Bayes(smoothness = numeric(0), Laplace = numeric(0)) %>% 
  set_engine("naivebayes") %>% 
## Naive Bayes Model Specification (classification)
## Main Arguments:
##   smoothness = numeric(0)
##   Laplace = numeric(0)
## Computational engine: naivebayes 
## Model fit template:
## naivebayes::naive_bayes(x = missing_arg(), y = missing_arg(), 
##     adjust = numeric(0), laplace = numeric(0), usekernel = TRUE)

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.

For count data, integers can be estimated using a Poisson distribution if the argument usepoisson = TRUE is passed as an engine argument.

Variance calculations are used in these computations so zero-variance predictors (i.e., with a single unique value) should be eliminated before fitting the model.

Case weights

The underlying model implementation does not allow for case weights.


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

parsnip documentation built on June 24, 2024, 5:14 p.m.