e1071 (version 1.7-17)

naiveBayes: Naive Bayes Classifier

Description

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

Usage

# S3 method for formula
naiveBayes(formula, data, laplace = 0, ..., subset, na.action = na.pass)
# S3 method for default
naiveBayes(x, y, laplace = 0, ...)

# S3 method for naiveBayes
predict(object, newdata,
  type = c("class", "raw"), threshold = 0.001, eps = 0, ...)

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.

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.

Examples

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

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

## using laplace smoothing:
model <- naiveBayes(Class ~ ., data = HouseVotes84, laplace = 3)
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))

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