metadata

license: unknown

Dataset Card for Wine Recognition dataset

Dataset Description

Homepage: https://archive.ics.uci.edu/ml/datasets/wine
Papers:
1. S. Aeberhard, D. Coomans and O. de Vel, Comparison of Classifiers in High Dimensional Settings, Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of Mathematics and Statistics, James Cook University of North Queensland.
2. S. Aeberhard, D. Coomans and O. de Vel, "THE CLASSIFICATION PERFORMANCE OF RDA" Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of Mathematics and Statistics, James Cook University of North Queensland.
Point of Contact: stefan'@'coral.cs.jcu.edu.au

Dataset Summary

These data are the results of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars. The analysis determined the quantities of 13 constituents found in each of the three types of wines. In a classification context, this is a well posed problem with "well behaved" class structures. A good data set for first testing of a new classifier, but not very challenging.

Supported Tasks and Leaderboards

Classification (cultivar) from continuous variables (all other variables)

Dataset Structure

Data Instances

178 wines

Data Fields

Wine category (cultivar)
Alcohol
Malic acid
Ash
Alcalinity of ash
Magnesium
Total phenols
Flavanoids
Nonflavanoid phenols
Proanthocyanins
Color intensity
Hue
OD280/OD315 of diluted wines
Proline

Data Splits

None

Dataset Creation

Source Data

https://archive.ics.uci.edu/ml/datasets/wine

Initial Data Collection and Normalization

Original Owners:

Forina, M. et al, PARVUS - An Extendible Package for Data Exploration, Classification and Correlation. Institute of Pharmaceutical and Food Analysis and Technologies, Via Brigata Salerno, 16147 Genoa, Italy.

Datasets:

katossky
/

wine-recognition