Cross-validation for the naive Bayes classifiers for compositional data using the
alfanb.tune(x, ina, a = seq(-1, 1, by = 0.1), type = "gaussian",
folds = NULL, nfolds = 10, stratified = TRUE, seed = NULL)A matrix with the available data, the predictor variables.
A vector of data. The response variable, which is categorical (factor is acceptable).
This can be a vector of values or a single number.
The type of naive Bayes, "gaussian", "cauchy" or "laplace".
A list with the indices of the folds.
The number of folds to be used. This is taken into consideration only if "folds" is NULL.
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.
You can specify your own seed number here or leave it NULL.
A list including:
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.
This function estimates the performance of the naive Bayes classifier for each value of
Friedman J., Hastie T. and Tibshirani R. (2017). The elements of statistical learning. New York: Springer.
# NOT RUN {
x <- as.matrix(iris[, 1:4])
x <- x / rowSums(x)
mod <- alfanb.tune(x, ina = iris[, 5], a = c(0, 0.1, 0.2) )
# }
Run the code above in your browser using DataLab