Classifying wine varieties

### Context

Wine recognition dataset from UC Irvine. Great for testing out different classifiers

**Labels:**
"name" - Number denoting a specific wine class

Number of instances of each wine class

- Class 1 - 59
- Class 2 - 71
- Class 3 - 48

**Features:**

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

### Content

"This data set is the result 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"

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

@misc{Lichman:2013 ,
author = "M. Lichman",
year = "2013",
title = "{UCI} Machine Learning Repository",
url = "http://archive.ics.uci.edu/ml",
institution = "University of California, Irvine, School of Information and Computer Sciences" }

UC Irvine data base: "https://archive.ics.uci.edu/ml/machine-learning-databases/wine"

Sources:
(a) 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.
(b) Stefan Aeberhard, email: [e-mail]
(c) July 1991
Past Usage:
(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).

### Inspiration

This data set is great for drawing comparisons between algorithms and testing out classifications models when learning new techniques

{'Context': '该数据集源自加州大学欧文分校，为一组葡萄酒识别数据集，适用于不同分类器的测试与验证。', '### Content': '本数据集是对在意大利同一地区种植但源自三种不同品种的葡萄酒进行化学分析的结果。分析确定了三种葡萄酒类型中发现的13种成分的含量。', '**Labels**': '“名称” - 代表特定葡萄酒类别的数字

各类葡萄酒实例的数量

- 类别 1 - 59 个
- 类别 2 - 71 个
- 类别 3 - 48 个', '@misc{Lichman:2013 ,
author = "M. Lichman",
year = "2013",
title = "{UCI}机器学习资源库",
url = "http://archive.ics.uci.edu/ml",
institution = "加州大学欧文分校，信息与计算机科学学院" }

UC Irvine数据库："https://archive.ics.uci.edu/ml/machine-learning-databases/wine"': 'Sources": " (a) Forina, M. 等人，PARVUS - 一种可扩展的数据探索、分类和相关性分析软件包。意大利热那亚，药食分析技术与研究所，维亚·布里加塔·萨勒诺街。
(b) Stefan Aeberhard，邮箱：[e-mail]
(c) 1991年7月
Past Usage:
(1) S. Aeberhard，D. Coomans 和 O. de Vel，《在高维设置中比较分类器》，技术报告第92-02号，（1992年），詹姆斯·库克大学计算机科学系和数学与统计系。
（亦提交至Technometrics）。
数据被用于与其他多个数据集一起比较各种分类器。尽管只有RDA实现了100%的正确分类率，但类别间是可区分的。
（RDA : 100%，QDA 99.4%，LDA 98.9%，1NN 96.1%（z变换数据）
（所有结果均使用留一法技术获得）
(2) S. Aeberhard，D. Coomans 和 O. de Vel，《RDA的分类性能》，技术报告第92-01号，（1992年），詹姆斯·库克大学计算机科学系和数学与统计系。
（亦提交至化学计量学杂志）。', '### Inspiration': '该数据集非常适合在掌握新技术时，对算法进行比较及测试分类模型。', '### Acknowledgements': 'Lichman, M. (2013). UCI机器学习资源库[http://archive.ics.uci.edu/ml]。加州，欧文：加州大学信息与计算机科学学院。', '**Features**': '1. 酒精含量
2. 苹果酸含量
3. 灰分含量
4. 灰分的碱性
5. 镁含量
6. 总酚含量
7. 黄烷醇含量
8. 非黄烷醇酚含量
9. 单宁含量
10. 颜色强度
11. 色调
12. 稀释葡萄酒的 OD280/OD315 值
13. 毛细管渗透压'}