naivebayes: Naive Bayes classifier

In targeted: Targeted Inference

Naive Bayes classifier

Description

Naive Bayes Classifier

Usage

naivebayes(
  formula,
  data,
  weights = NULL,
  kernel = FALSE,
  laplace.smooth = 0,
  prior = NULL,
  ...
)

Arguments

formula	Formula with syntax: response ~ predictors | weights
data	data.frame
weights	optional frequency weights
kernel	If TRUE a kernel estimator is used for numeric predictors (otherwise a gaussian model is used)
laplace.smooth	Laplace smoothing
prior	optional prior probabilities (default estimated from data)
...	additional arguments to lower level functions

Value

An object of class 'naivebayes' is returned. See naivebayes-class for more details about this class and its generic functions.

Author(s)

Klaus K. Holst

Examples

library(data.table)
data(iris)
m <- naivebayes(Species ~ Sepal.Width + Petal.Length, data = iris)
pr <- predict(m, newdata = iris)

# using weights to reduce the size of the dataset
n <- 5e2
x <- rnorm(n, sd = 2) > 0
y <- rbinom(n, 1, lava::expit(x))
# full data set
d1 <- data.frame(y, x = as.factor(x > 0))
m1 <- naivebayes(y ~ x, data = d1)
# reduced data set
d2 <- data.table(d1)[, .(.N), by = .(y, x)]
m2 <- naivebayes(y ~ x, data = d2, weights = d2$N)
all(predict(m1, d1) == predict(m2, d1))

targeted documentation built on Jan. 12, 2026, 9:08 a.m.