wine: Wine
In tyluRp/ucimlr: UCI Machine Learning Repository
Description
Usage
Format
Details
Source
References
Description
Wine recognition data. 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.
Usage
1
Format
A data frame with 178 observations on the following 14 variables.
Type: The type of wine, into one of three classes, 1 (59 obs), 2(71 obs), and 3 (48 obs).
Alcohol
Malic acid
Ash
Alcalinity of ash
Magnesium
Total phenols
Flavanoids
Nonflavanoid phenols
Proanthocyanins
Color intensity
Hue
Dilution: D280/OD315 of diluted wines
Proline
Details
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.
I think that the initial data set had around 30 variables, but for some
reason I only have the 13 dimensional version. I had a list of what the 30
or so variables were, but a.) I lost it, and b.), I would not know which 13
variables are included in the set.
Source
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.
Donor:
Stefan Aeberhard, email: stefan '@' coral.cs.jcu.edu.au
References
(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.
(Also submitted to Technometrics).
The data was used with many others for comparing various
classifiers. The classes are separable, though only RDA
has achieved 100
(RDA : 100
(All results using the leave-one-out technique)
(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.
(Also submitted to Journal of Chemometrics).
Here, the data was used to illustrate the superior performance of
the use of a new appreciation function with RDA.
https://archive.ics.uci.edu/ml/machine-learning-databases/wine/
https://archive.ics.uci.edu/ml/datasets/Wine
tyluRp/ucimlr documentation built on Feb. 2, 2021, 6:51 a.m.
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Description Usage Format Details Source References
Description
Wine recognition data. 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.
Usage
1 |
Format
A data frame with 178 observations on the following 14 variables.
Type: The type of wine, into one of three classes, 1 (59 obs), 2(71 obs), and 3 (48 obs).
Alcohol
Malic acid
Ash
Alcalinity of ash
Magnesium
Total phenols
Flavanoids
Nonflavanoid phenols
Proanthocyanins
Color intensity
Hue
Dilution: D280/OD315 of diluted wines
Proline
Details
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.
I think that the initial data set had around 30 variables, but for some reason I only have the 13 dimensional version. I had a list of what the 30 or so variables were, but a.) I lost it, and b.), I would not know which 13 variables are included in the set.
Source
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.
Donor: Stefan Aeberhard, email: stefan '@' coral.cs.jcu.edu.au
References
(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. (Also submitted to Technometrics).
The data was used with many others for comparing various classifiers. The classes are separable, though only RDA has achieved 100 (RDA : 100 (All results using the leave-one-out technique)
(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. (Also submitted to Journal of Chemometrics).
Here, the data was used to illustrate the superior performance of the use of a new appreciation function with RDA.
https://archive.ics.uci.edu/ml/machine-learning-databases/wine/
https://archive.ics.uci.edu/ml/datasets/Wine
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