predict.NaiveBayes: Naive Bayes Classifier

Description Usage Arguments Details Value Author(s) See Also Examples

Description

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

Usage

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## S3 method for class '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.

Author(s)

Karsten Luebke, [email protected]

See Also

NaiveBayes,dkernelnaiveBayes,qda

Examples

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

klaR documentation built on May 1, 2019, 8:48 p.m.