A data set of results from chemical analysis of wines grown in Italy from three different cultivars.
178 observations from 13 variables represented as a list consisting
of a categorical response vector
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:
alcalinity of ash
OD280/OD315 of diluted wines
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.
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