predict.nb2: Prediction for Extended Naive Bayes (nb2)

In aschersleben/NBCD: Naive Bayes for Online Classification with Concept Drift

Description

Modified and extended copy of predict.naiveBayes from e1071.

Usage

## S3 method for class 'nb2'
predict(object, newdata, type = c("class", "raw"),
  threshold = 0.001, eps = 0, ...)

Arguments

object	An object of class "nb2".
newdata	A dataframe with new predictors (with possibly fewer columns than the training data). Note that the column names of newdata are matched against the training data ones.
type	If "raw", the conditional a-posterior probabilities for each class are returned, and the class with maximal probability else.
threshold	Value replacing cells with probabilities within eps range.
eps	double for specifying an epsilon-range to apply laplace smoothing (to replace zero or close-zero probabilities by theshold.)
...	Currently ignored.

Author(s)

• David Meyer [e-mail]: original predict.naiveBayes

• Philipp Aschersleben [e-mail]: modifications and extensions for the NBCD package

See Also

nb2, update.nb2, plot.nb2, print.nb2

Examples

mod <- nb2(iris[, 1:4], iris[, 5])
predict(mod, iris)

discParam <- list(Sepal.L = c(5, 6, 7), Sepal.W = c(2.5, 3.5))
mod2 <- nb2(iris[, 1:4], iris[, 5], discretize = "fixed", discParam = discParam)
predict(mod2, iris)

c(mean(predict(mod, iris) == iris$Species),
  mean(predict(mod2, iris) == iris$Species))

aschersleben/NBCD documentation built on May 12, 2019, 4:32 a.m.