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KEEL-dataset - data set description
dataset/images/wine.jpg



This section describes main characteristics of the wine data set and its attributes:

General information

Wine data set
TypeClassificationOriginReal world
Features 13(Real / Integer / Nominal)(13 / 0 / 0)
Instances178 Classes3
Missing values?No

Attribute description

AttributeDomainAttributeDomain
Alcohol[11.0, 14.9]Nonflavanoid_phenols[0.1, 0.7]
Malic_acid[0.7, 5.8]Proanthocyanins[0.4, 3.6]
Ash[1.3, 3.3]Color_intensity[1.2, 13.0]
Alcalinity_of_ash[10.6, 30.0]Hue[0.4, 1.8]
Magnesium[70.0, 162.0]OD280/OD315[1.2, 4.0]
Total_phenols[0.9, 3.9]Proline[278.0, 1680.0]
Flavanoids[0.3, 5.1]Class{1,2,3}

Additional information

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.




In this section you can download some files related to the wine data set:

  • The complete data set already formatted in KEEL format can be downloaded from herezip.gif.
  • A copy of the data set already partitioned by means of a 10-folds cross validation procedure can be downloaded from herezip.gif.
  • A copy of the data set already partitioned by means of a 5-folds cross validation procedure can be downloaded from herezip.gif.
  • The header file associated to this data set can be downloaded from heretxt.png.
  • This is not a native data set from the KEEL project. It has been obtained from the UCI Machine Learning Repository. The original page where the data set can be found is: http://archive.ics.uci.edu/ml/datasets/Wine.


 
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