Data
WineDataset

WineDataset

active ARFF CC0: Public Domain Visibility: public Uploaded 23-03-2022 by Dustin Carrion
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  • Earth Science Machine Learning
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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. 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. The attributes are (dontated by Riccardo Leardi, riclea '' anchem.unige.it ) 1) Alcohol 2) Malic acid 3) Ash 4) Alcalinity 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 well posed problem with "well behaved" class structures. A good data set for first testing of a new classifier, but not very challenging.

14 features

Alcoholnumeric126 unique values
0 missing
Malic_Acidnumeric133 unique values
0 missing
Ashnumeric79 unique values
0 missing
Alcalinity_of_ashnumeric63 unique values
0 missing
Magnesiumnumeric53 unique values
0 missing
Total_phenolsnumeric97 unique values
0 missing
Flavanoidsnumeric132 unique values
0 missing
Nonflavanoid_phenolsnumeric39 unique values
0 missing
Proanthocyaninsnumeric101 unique values
0 missing
Color_intensitynumeric132 unique values
0 missing
Huenumeric78 unique values
0 missing
OD280/OD315_of_diluted_winesnumeric122 unique values
0 missing
Prolinenumeric121 unique values
0 missing
Winenumeric3 unique values
0 missing

19 properties

178
Number of instances (rows) of the dataset.
14
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
14
Number of numeric attributes.
0
Number of nominal attributes.
0.08
Number of attributes divided by the number of instances.
100
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
Average class difference between consecutive instances.
0
Percentage of missing values.

1 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature:
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