R/wine_docs.R

In coatless/ucidata: Collection of Datasets from the UC Irvine Machine Learning Repository

#' Wine Data Set
#'
#' This data set is the combination of two datasets that were created, using red and white wine samples.
#' The inputs include objective tests (e.g. PH values) and the output is based on sensory data
#' (median of at least 3 evaluations made by wine experts). Each expert graded the wine quality
#' between 0 (very bad) and 10 (very excellent). Several data mining methods were applied to model
#' these datasets under a regression approach. The support vector machine model achieved the
#' best results. Several metrics were computed: MAD, confusion matrix for a fixed error tolerance (T),
#' etc. Also, we plot the relative importances of the input variables (as measured by a sensitivity
#'                                                                     analysis procedure).
#' @format A data frame with 6497 observations (1599 Red and 4898 White) on the following 12 variables.
#' - fixed acidity
#' - volatile acidity
#' - citric acid
#' - residual sugar
#' - chlorides
#' - free sulfur dioxide
#' - total sulfur dioxide
#' - density
#' - pH
#' - sulphates
#' - alcohol
#' - quality
#'     - Score between 0 and 10 based on sensor reading
#' - color
#'     - `"White"` or `"Red"`
#' @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. ISSN: 0167-9236.
#' @references
#' <https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality.names>
#' <https://archive.ics.uci.edu/ml/datasets/Wine+Quality>
"wine"