This dataset contains measurements of various chemical properties of white wines along with their quality ratings and a quality classification. The dataset was obtained from the UCI Machine Learning Repository.
The data is provided as a list with two components:
A data frame containing the chemical properties of the wines. The variables include:
Fixed acidity (g/L).
Volatile acidity (g/L), mainly due to acetic acid.
Citric acid (g/L).
Residual sugar (g/L).
Chloride concentration (g/L).
Free sulfur dioxide (mg/L).
Total sulfur dioxide (mg/L).
Density of the wine (g/cm\(^3\)).
pH value of the wine.
Sulphates (g/L).
Alcohol content (% by volume).
A list containing two elements:
A numeric vector representing the wine quality scores (used as the regression target).
A factor with levels "High Quality", "Medium Quality", and "Low Quality",
where classification is determined as follows:
quality \(\geq\) 7.
quality \(\leq\) 4.
for all other quality scores.
data(wine.quality)A list with two components:
A data frame with 11 chemical property variables.
A list with two elements: regression (wine quality scores) and class (quality classification).
Cortez, Paulo, et al. "Modeling wine preferences by data mining from physicochemical properties." Decision support systems 47.4 (2009): 547-553.