ucimlr: UCI Machine Learning Repository

#' Wine Quality
#'
#' This dataset is related to red and white vinho verde wine samples, from the
#' north of Portugal. The goal is to model wine quality based on
#' physicochemical tests, see: http://www3.dsi.uminho.pt/pcortez/wine/. 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.).
#'
#' @format A data frame with 6497 observations on the following 13 variables.
#' \enumerate{
#'   \item fixed acidity
#'   \item volatile acidity
#'   \item citric acid
#'   \item residual sugar
#'   \item chlorides
#'   \item free sulfur dioxide
#'   \item total sulfur dioxide
#'   \item density
#'   \item ph
#'   \item sulphates
#'   \item alcohol: Output variable (based on sensory data)
#'   \item quality: (score between 0 and 10)
#' }
#'
#' @details
#' These datasets can be viewed as classification or regression tasks. The
#' classes are ordered and not balanced (e.g. there are munch more normal wines
#' than excellent or poor ones). Outlier detection algorithms could be used to
#' detect the few excellent or poor wines. Also, we are not sure if all input
#' variables are relevant. So it could be interesting to test feature selection
#' methods.
#'
#' @references
#' 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.
#'
#' https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/
#'
#' https://archive.ics.uci.edu/ml/datasets/Wine+Quality
#'
#' http://www3.dsi.uminho.pt/pcortez/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.
#' '@'2009
"wine_quality"