Naive Bayes Classifier

Description

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

Usage

predict.naiveBayes(object, newdata, type = c("class", "raw"), threshold = 0.001, ...)

Arguments

Details

The standard naive Bayes classifier (at least this implementation) assumes independence of the predictor variables, and gaussian distribution (given the target class) of metric predictors. For attributes with missing values, the corresponding table entries are omitted for prediction.

Value

If type = "raw", the conditional a-posterior probabilities for each class are returned, and the class with maximal probability else.

Author(s)

David Meyer David.Meyer@R-project.org

See Also

naiveBayes

Examples

## Categorical data only:
data(HouseVotes84)
model <- naiveBayes(Class ~ ., data = HouseVotes84)
predict(model, HouseVotes84[1:10,-1])
predict(model, HouseVotes84[1:10,-1], type = "raw")

pred <- predict(model, HouseVotes84[,-1])
table(pred, HouseVotes84$Class)

## Example of using a contingency table:
data(Titanic)
m <- naiveBayes(Survived ~ ., data = Titanic)
m
predict(m, as.data.frame(Titanic)[,1:3])

## Example with metric predictors:
data(iris)
m <- naiveBayes(Species ~ ., data = iris)
## alternatively:
m <- naiveBayes(iris[,-5], iris[,5])
m
table(predict(m, iris[,-5]), iris[,5])