Wine
Donated on 6/30/1991
Using chemical analysis to determine the origin of wines
Dataset Characteristics
Tabular
Subject Area
Physics and Chemistry
Associated Tasks
Classification
Feature Type
Integer, Real
# Instances
178
# Features
13
Dataset Information
For what purpose was the dataset created?
test
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. I think that the initial data set had around 30 variables, but for some reason I only have the 13 dimensional version. I had a list of what the 30 or so variables were, but a.) I lost it, and b.), I would not know which 13 variables are included in the set. The attributes are (dontated by Riccardo Leardi, riclea@anchem.unige.it ) 1) Alcohol 2) Malic acid 3) Ash 4) Alcalinity of ash 5) Magnesium 6) Total phenols 7) Flavanoids 8) Nonflavanoid phenols 9) Proanthocyanins 10)Color intensity 11)Hue 12)OD280/OD315 of diluted wines 13)Proline 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.
Has Missing Values?
No
Introductory Paper
By S. Aeberhard, D. Coomans, O. Vel. 1994
Published in Pattern Recognition
Variables Table
| Variable Name | Role | Type | Description | Units | Missing Values |
|---|---|---|---|---|---|
| class | Target | Categorical | no | ||
| Alcohol | Feature | Continuous | no | ||
| Malicacid | Feature | Continuous | no | ||
| Ash | Feature | Continuous | no | ||
| Alcalinity_of_ash | Feature | Continuous | no | ||
| Magnesium | Feature | Integer | no | ||
| Total_phenols | Feature | Continuous | no | ||
| Flavanoids | Feature | Continuous | no | ||
| Nonflavanoid_phenols | Feature | Continuous | no | ||
| Proanthocyanins | Feature | Continuous | no |
0 to 10 of 14
Additional Variable Information
All attributes are continuous No statistics available, but suggest to standardise variables for certain uses (e.g. for us with classifiers which are NOT scale invariant) NOTE: 1st attribute is class identifier (1-3)
Baseline Model Performance
Dataset Files
| File | Size |
|---|---|
| wine.data | 10.5 KB |
| wine.names | 3 KB |
| Index | 105 Bytes |
Papers Citing this Dataset
Sort by Year, desc
By Shinpei Imori, Hidetoshi Shimodaira. 2019
Published in Entropy.
By Hongkang Yang, Esteban Tabak. 2019
Published in
By Takanori Fujiwara, Oh-Hyun Kwon, Kwan-Liu Ma. 2019
Published in ArXiv.
0 to 5 of 131
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset wine = fetch_ucirepo(id=109) # data (as pandas dataframes) X = wine.data.features y = wine.data.targets # metadata print(wine.metadata) # variable information print(wine.variables)
Aeberhard, S. & Forina, M. (1992). Wine [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5PC7J.
Keywords
Creators
Stefan Aeberhard
M. Forina
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.