A data set of results from chemical analysis of wines grown in Italy from three different cultivars.

wine

Format

178 observations from 13 variables represented as a list consisting of a categorical response vector y with three levels: A, B, and C representing different cultivars of wine as well as x: a sparse feature matrix of class 'dgCMatrix' with the following variables:

alcohol

alcoholic content

malic

malic acid

ash

ash

alcalinity

alcalinity of ash

magnesium

magnemium

phenols

total phenols

flavanoids

flavanoids

nonflavanoids

nonflavanoid phenols

proanthocyanins

proanthocyanins

color

color intensity

hue

hue

dilution

OD280/OD315 of diluted wines

proline

proline

Source

Dua, D. and Karra Taniskidou, E. (2017). UCI Machine Learning Repository http://archive.ics.uci.edu/ml. Irvine, CA: University of California, School of Information and Computer Science.

https://raw.githubusercontent.com/hadley/rminds/master/1-data/wine.csv

https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass.html#wine