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