Wine recognition dataset

Data Set Characteristics:

Number of Instances:

178

Number of Attributes:

13 numeric, predictive attributes and the class

Attribute Information:
  • Alcohol

  • Malic acid

  • Ash

  • Alcalinity of ash

  • Magnesium

  • Total phenols

  • Flavanoids

  • Nonflavanoid phenols

  • Proanthocyanins

  • Color intensity

  • Hue

  • OD280/OD315 of diluted wines

  • Proline

  • class:
    • class_0

    • class_1

    • class_2

Summary Statistics:

Alcohol:

11.0

14.8

13.0

0.8

Malic Acid:

0.74

5.80

2.34

1.12

Ash:

1.36

3.23

2.36

0.27

Alcalinity of Ash:

10.6

30.0

19.5

3.3

Magnesium:

70.0

162.0

99.7

14.3

Total Phenols:

0.98

3.88

2.29

0.63

Flavanoids:

0.34

5.08

2.03

1.00

Nonflavanoid Phenols:

0.13

0.66

0.36

0.12

Proanthocyanins:

0.41

3.58

1.59

0.57

Colour Intensity:

1.3

13.0

5.1

2.3

Hue:

0.48

1.71

0.96

0.23

OD280/OD315 of diluted wines:

1.27

4.00

2.61

0.71

Proline:

278

1680

746

315

Missing Attribute Values:

None

Class Distribution:

class_0 (59), class_1 (71), class_2 (48)

Creator:

R.A. Fisher

Donor:

Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)

Date:

July, 1988

This is a copy of UCI ML Wine recognition datasets. https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data

The data is the results of a chemical analysis of wines grown in the same region in Italy by three different cultivators. There are thirteen different measurements taken for different constituents found in the three types of wine.

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.

Citation:

Lichman, M. (2013). UCI Machine Learning Repository [https://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.

|details-start| References |details-split|

(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. (Also submitted to Technometrics).

The data was used with many others for comparing various classifiers. The classes are separable, though only RDA has achieved 100% correct classification. (RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data)) (All results using the leave-one-out technique)

(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. (Also submitted to Journal of Chemometrics).

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