Naive Bayes models via naivebayes
Source:R/naive_Bayes_naivebayes.R
details_naive_Bayes_naivebayes.Rdnaivebayes::naive_bayes() fits a model that uses Bayes' theorem to compute
the probability of each class, given the predictor values.
Details
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.
library(discrim)
naive_Bayes(smoothness = numeric(0), Laplace = numeric(0)) %>%
set_engine("naivebayes") %>%
translate()## 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.