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Wine recognition dataset
Data Set Characteristics:


Number of Instances:

178 (50 in each of three classes)


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.

References
(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).