# predict.NaiveBayes

##### Naive Bayes Classifier

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.

##### 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.

##### Value

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

##### See Also

##### Examples

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

*Documentation reproduced from package klaR, version 0.6-14, License: GPL-2*