Wine (Wine Data Set)

“这些数据是对在意大利同一地区种植但来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中每种葡萄酒的 13 种成分的数量。我认为初始数据集有大约 30 个变量，但由于某种原因，我只有 13 维版本。我有一个 30 个左右变量的列表，但是 a.) 我丢失了它，b.)，我不会知道集合中包含了哪些 13 个变量。属性是（由 Riccardo Leardi，riclea '@' anchem.unige.it 捐赠）1）酒精 2）苹果酸 3）灰分 4）灰分的碱度 5）镁 6）总计酚类 7) 类黄酮 8) 非黄酮类酚类 9) 原花青素 10) 颜色强度 11) 色调 12) 稀释葡萄酒的 OD280/OD315 13) 脯氨酸 在分类上下文中，这是一个“表现良好”的类结构的恰当问题。一个很好的数据集，用于首次测试新分类器，但不是很有挑战性。”

These data are the results of 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 believe the original dataset had approximately 30 variables, but for some reason I only have the 13-dimensional version. I have a list of roughly 30 variables, but a.) I have lost it, and b.) I would not know which 13 variables are included in this set. The attributes were donated by Riccardo Leardi (riclea '@' anchem.unige.it) as follows: 1) Alcohol 2) Malic acid 3) Ash 4) Alkalinity 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 suitable problem with a "well-behaved" class structure. It is an excellent dataset for testing new classifiers for the first time, but not very challenging.

搜集汇总

背景与挑战

背景概述

该数据集记录了意大利同一地区三种葡萄酒品种的化学分析结果，包含13种成分变量，如酒精和苹果酸。它适用于分类算法的初步测试，但挑战性相对较低。