R/wine.R

In treemisc: Data Sets and Functions to Accompany "Tree-Based Methods for Statistical Learning in R"

#' Wine quality
#'
#' These data are related to red and white variants of the Portuguese "Vinho 
#' Verde" wine. For details, see Cortez et al. (2009). Due to privacy and 
#' logistic issues, only physicochemical (inputs) and sensory (the output) 
#' variables are available (e.g., there is no data about grape types, wine 
#' brand, wine selling price, etc.). These data can be used for classification 
#' or regression tasks. The classes are ordered and not balanced (e.g., there 
#' are many more normal wines than excellent or poor ones). Outlier detection 
#' algorithms could be used to detect the few excellent or poor wines. Also, it 
#' is not known if all input variables are relevant. So it could be interesting 
#' to test feature selection methods.
#'
#' @docType data
#'
#' @keywords datasets
#'
#' @format A data frame with 6497 rows and 13 variables.
#' 
#' @details 
#' 
#' \describe{
#' 
#' \item{fixed.acidity}{Input feature (continuous).}
#' \item{volatile.acidity}{Input feature (continuous).}
#' \item{citric.acid}{Input feature (continuous).}
#' \item{residual.sugar}{Input feature (continuous).}
#' \item{chlorides}{Input feature (continuous).}
#' \item{free.sulfur.dioxide}{Input feature (continuous).}
#' \item{total.sulfur.dioxide}{Input feature (continuous).}
#' \item{density}{Input feature (continuous).}
#' \item{pH}{Input feature (continuous).}
#' \item{sulphates}{Input feature (continuous).}
#' \item{alcohol}{Input feature (continuous).}
#' \item{quality}{Target variable (continuous).}
#' \item{type}{Input feature specifying whether it's a red or white wine 
#' (factor).}
#' 
#' }
#'
#' @name wine
#'
#' @source
#' P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. 
#' Modeling wine preferences by data mining from physicochemical properties. 
#' In Decision Support Systems, Elsevier, 47(4):547-553, 2009.
NULL