Computes the conditional a-posterior probabilities of a categorical class variable given independent predictor variables using the Bayes rule.
# S3 method for NaiveBayes
predict(object, newdata, threshold = 0.001, ...)A list with the conditional a-posterior probabilities for each class and the estimated class are returned.
An object of class "naiveBayes".
A dataframe with new predictors.
Value replacing cells with 0 probabilities.
passed to dkernel function if neccessary.
Karsten Luebke, karsten.luebke@fom.de
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.
NaiveBayes,dkernelnaiveBayes,qda
data(iris)
m <- NaiveBayes(Species ~ ., data = iris)
predict(m)
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