nb.cv: Cross-validation for the naive Bayes classifiers

In Rfast2: A Collection of Efficient and Extremely Fast R Functions II

Cross-validation for the naive Bayes classifiers

Description

Cross-validation for the naive Bayes classifiers.

Usage

nb.cv(x, ina, type = "gaussian", folds = NULL, nfolds = 10, 
      stratified = TRUE, seed = FALSE, pred.ret = FALSE)

Arguments

x	A matrix with the available data, the predictor variables.
ina	A vector of data. The response variable, which is categorical (factor is acceptable).
type	The type of naive Bayes, "gaussian", "gamma", "weibull", "normlog", "laplace", "cauchy", "logitnorm", "beta", "vm" or "spml", "poisson", "multinom", "geom" or "bernoulli".
folds	A list with the indices of the folds.
nfolds	The number of folds to be used. This is taken into consideration only if "folds" is NULL.
stratified	Do you want the folds to be selected using stratified random sampling? This preserves the analogy of the samples of each group. Make this TRUE if you wish.
seed	If you set this to TRUE, the same folds will be created every time.
pred.ret	If you want the predicted values returned set this to TRUE.

Value

A list including:

preds	If pred.ret is TRUE the predicted values for each fold are returned as elements in a list.
crit	A vector whose length is equal to the number of k and is the accuracy metric for each k. For the classification case it is the percentage of correct classification. If you want to compute other metrics of accuracy we suggest you choose "pred.ret = TRUE" when running the function and then write a simple function to compute more metrics. See .

Author(s)

Michail Tsagris.

R implementation and documentation: Michail Tsagris [e-mail].

References

Friedman J., Hastie T. and Tibshirani R. (2017). The elements of statistical learning. New York: Springer.

See Also

weibullnb.pred, weibull.nb, vm.nb, vmnb.pred, mle.lda, reg.mle.lda, multinom.reg

Examples

x <- as.matrix(iris[, 1:4])
mod <- nb.cv(ina = iris[, 5], x = x )

Rfast2 documentation built on May 29, 2024, 8:45 a.m.