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.. _wine_dataset: |
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Wine recognition dataset |
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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%[email protected]) |
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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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.. dropdown:: References |
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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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