# NaiveBayes

From klaR v0.6-11
by Uwe Ligges

##### 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 class 'formula':
NaiveBayes(formula, data, ..., subset, na.action = na.pass)
## S3 method for class 'default':
NaiveBayes(x, grouping, prior, usekernel = FALSE, fL = 0, ...)
```

##### Arguments

- x
- a numeric matrix, or a data frame of categorical and/or numeric variables.
- grouping
- class vector (a factor).
- formula
- a formula of the form
`class ~ x1 + x2 + ...`

. Interactions are not allowed. - data
- a data frame of predictors (caegorical and/or numeric).
- prior
- the prior probabilities of class membership. If unspecified, the class proportions for the training set are used. If present, the probabilities should be specified in the order of the factor levels.
- usekernel
- if
`TRUE`

a kernel density estimate (`density`

) is used for denstity estimation. If`FALSE`

a normal density is estimated. - fL
- Factor for Laplace correction, default factor is 0, i.e. no correction.
- ...
- arguments passed to
`density`

. - subset
- for data given in a data frame, an index vector specifying the cases to be used in the training sample. (NOTE: If given, this argument must be named.)
- na.action
- a function to specify the action to be taken if
`NA`

s are found. The default action is not to count them for the computation of the probability factors. An alternative is na.omit, which leads to rejection of cas

##### 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 and user
specified `prior`

probabilities.
The standard naive Bayes classifier (at least this implementation)
assumes independence of the predictor
variables.

##### Value

- An object of class
`"NaiveBayes"`

including components: apriori Class distribution for the dependent variable. tables A list of tables, one for each predictor variable. For each categorical variable a table giving, for each attribute level, the conditional probabilities given the target class. For each numeric variable, a table giving, for each target class, mean and standard deviation of the (sub-)variable or a object of `class`

`density`

.

##### concept

- Naive Bayes Classification
- Kernel estimated densities

##### See Also

##### Examples

```
data(iris)
m <- NaiveBayes(Species ~ ., data = iris)
```

*Documentation reproduced from package klaR, version 0.6-11, License: GPL-2*

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