99 lines
3.3 KiB
ReStructuredText
99 lines
3.3 KiB
ReStructuredText
.. _wine_dataset:
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Wine recognition dataset
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------------------------
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**Data Set Characteristics:**
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:Number of Instances: 178
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:Number of Attributes: 13 numeric, predictive attributes and the class
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:Attribute Information:
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- Alcohol
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- Malic acid
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- Ash
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- Alcalinity of ash
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- Magnesium
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- Total phenols
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- Flavanoids
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- Nonflavanoid phenols
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- Proanthocyanins
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- Color intensity
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- Hue
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- OD280/OD315 of diluted wines
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- Proline
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- class:
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- class_0
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- class_1
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- class_2
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:Summary Statistics:
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============================= ==== ===== ======= =====
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Min Max Mean SD
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============================= ==== ===== ======= =====
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Alcohol: 11.0 14.8 13.0 0.8
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Malic Acid: 0.74 5.80 2.34 1.12
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Ash: 1.36 3.23 2.36 0.27
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Alcalinity of Ash: 10.6 30.0 19.5 3.3
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Magnesium: 70.0 162.0 99.7 14.3
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Total Phenols: 0.98 3.88 2.29 0.63
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Flavanoids: 0.34 5.08 2.03 1.00
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Nonflavanoid Phenols: 0.13 0.66 0.36 0.12
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Proanthocyanins: 0.41 3.58 1.59 0.57
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Colour Intensity: 1.3 13.0 5.1 2.3
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Hue: 0.48 1.71 0.96 0.23
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OD280/OD315 of diluted wines: 1.27 4.00 2.61 0.71
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Proline: 278 1680 746 315
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============================= ==== ===== ======= =====
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:Missing Attribute Values: None
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:Class Distribution: class_0 (59), class_1 (71), class_2 (48)
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:Creator: R.A. Fisher
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:Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
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:Date: July, 1988
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This is a copy of UCI ML Wine recognition datasets.
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https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data
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The data is the results of a chemical analysis of wines grown in the same
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region in Italy by three different cultivators. There are thirteen different
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measurements taken for different constituents found in the three types of
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wine.
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Original Owners:
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Forina, M. et al, PARVUS -
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An Extendible Package for Data Exploration, Classification and Correlation.
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Institute of Pharmaceutical and Food Analysis and Technologies,
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Via Brigata Salerno, 16147 Genoa, Italy.
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Citation:
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Lichman, M. (2013). UCI Machine Learning Repository
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[https://archive.ics.uci.edu/ml]. Irvine, CA: University of California,
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School of Information and Computer Science.
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|details-start|
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**References**
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|details-split|
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(1) S. Aeberhard, D. Coomans and O. de Vel,
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Comparison of Classifiers in High Dimensional Settings,
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Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of
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Mathematics and Statistics, James Cook University of North Queensland.
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(Also submitted to Technometrics).
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The data was used with many others for comparing various
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classifiers. The classes are separable, though only RDA
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has achieved 100% correct classification.
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(RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data))
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(All results using the leave-one-out technique)
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(2) S. Aeberhard, D. Coomans and O. de Vel,
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"THE CLASSIFICATION PERFORMANCE OF RDA"
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Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of
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Mathematics and Statistics, James Cook University of North Queensland.
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(Also submitted to Journal of Chemometrics).
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|details-end|
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