katossky/wine-recognition

---
annotations_creators:
- no-annotation
language: []
language_creators:
- expert-generated
license:
- unknown
pretty_name: Wine Recognition Dataset
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- tabular-classification
task_ids:
- tabular-multi-class-classification
---

# Dataset Card for Wine Recognition dataset

## Dataset Description

- **Homepage:** https://archive.ics.uci.edu/ml/datasets/wine
- **Papers:**
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.
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.
- **Point of Contact:** stefan'@'coral.cs.jcu.edu.au

### Dataset Summary

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.

### Supported Tasks and Leaderboards

Classification (cultivar) from continuous variables (all other variables)

## Dataset Structure

### Data Instances

178 wines

### Data Fields

1. Wine category (cultivar)
2. Alcohol
3. Malic acid
4. Ash
5. Alcalinity of ash
6. Magnesium
7. Total phenols
8. Flavanoids
9. Nonflavanoid phenols
10. Proanthocyanins
11. Color intensity
12. Hue
13. OD280/OD315 of diluted wines
14. Proline

### Data Splits

None

## Dataset Creation

### Source Data

https://archive.ics.uci.edu/ml/datasets/wine

#### Initial Data Collection and Normalization

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.

## Additional Information

### Dataset Curators

Stefan Aeberhard

### Licensing Information

No information found on the original website

原始信息汇总

Wine Recognition Dataset 概述

数据集描述

• 数据集名称: Wine Recognition Dataset
• 数据集大小: 小于1000条记录
• 数据集来源: 原始数据
• 任务类别: 表格分类
• 具体任务: 多类别表格分类
• 许可证: 未知
• 语言和创建者: 专家生成

数据集概要

该数据集包含178种葡萄酒的化学分析结果，这些葡萄酒来自意大利同一地区，但来自三种不同的栽培品种。分析确定了每种葡萄酒中的13种成分的含量。在分类背景下，这是一个结构良好的问题，具有“良好行为”的类结构。适合新分类器的首选测试数据集，但挑战性不大。

数据集结构

数据实例

• 总数: 178种葡萄酒

数据字段

1. 葡萄酒类别（栽培品种）
2. 酒精
3. 苹果酸
4. 灰分
5. 灰分的碱度
6. 镁
7. 总酚
8. 类黄酮
9. 非类黄酮酚
10. 原花青素
11. 颜色强度
12. 色调
13. OD280/OD315稀释葡萄酒
14. 脯氨酸

数据分割

• 分割情况: 无

搜集汇总

背景与挑战

背景概述

这是一个用于葡萄酒品种分类的小型表格数据集，包含178个样本，每个样本有13个化学特征（如酒精含量、苹果酸等）和一个标签（表示三个葡萄品种）。该数据集适用于多类分类任务的初步测试，但挑战性较低，常用于机器学习算法的基准验证。