# predict.NaiveBayes: Naive Bayes Classifier

## Description

Computes the conditional a-posterior probabilities of a categorical
class variable given independent predictor variables using
the Bayes rule.

## Usage

# S3 method for NaiveBayes
predict(object, newdata, threshold = 0.001, ...)

## Arguments

object

An object of class `"naiveBayes"`

.

newdata

A dataframe with new predictors.

threshold

Value replacing cells with 0 probabilities.

…

passed to `dkernel`

function if neccessary.

## Value

A list with the conditional a-posterior
probabilities for each class and the estimated class are returned.

## Details

This implementation of Naive Bayes as well as this help is based on the code by
David Meyer in the package e1071 but extended for kernel estimated densities.
The standard naive Bayes classifier (at least this implementation)
assumes independence of the predictor
variables. For attributes with missing values, the
corresponding table entries are omitted for prediction.

## Examples

# NOT RUN {
data(iris)
m <- NaiveBayes(Species ~ ., data = iris)
predict(m)
# }