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

Arguments

A numeric matrix, or a data frame of categorical and/or numeric variables.

Class vector.

formula

A formula of the form

class ~ x1 + x2 +
      ...

. Interactions are not allowed.

data

Either a data frame of predictors (caegorical and/or numeric) or a contingency table.

laplace

positive double controlling Laplace smoothing. The default (0) disables Laplace smoothing.

...

Currently not used.

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 NAs 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 cases

Value

An object of class "naiveBayes" including components:
aprioriClass distribution for the dependent variable.
tablesA 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.

Examples

Run this code

## 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)

## 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)[,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])