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+.. _wine_dataset:
+
+Wine recognition dataset
+------------------------
+
+**Data Set Characteristics:**
+
+    :Number of Instances: 178 (50 in each of three classes)
+    :Number of Attributes: 13 numeric, predictive attributes and the class
+    :Attribute Information:
+ 		- Alcohol
+ 		- Malic acid
+ 		- Ash
+		- Alcalinity of ash  
+ 		- Magnesium
+		- Total phenols
+ 		- Flavanoids
+ 		- Nonflavanoid phenols
+ 		- Proanthocyanins
+		- Color intensity
+ 		- Hue
+ 		- OD280/OD315 of diluted wines
+ 		- Proline
+
+    - class:
+            - class_0
+            - class_1
+            - class_2
+		
+    :Summary Statistics:
+    
+    ============================= ==== ===== ======= =====
+                                   Min   Max   Mean     SD
+    ============================= ==== ===== ======= =====
+    Alcohol:                      11.0  14.8    13.0   0.8
+    Malic Acid:                   0.74  5.80    2.34  1.12
+    Ash:                          1.36  3.23    2.36  0.27
+    Alcalinity of Ash:            10.6  30.0    19.5   3.3
+    Magnesium:                    70.0 162.0    99.7  14.3
+    Total Phenols:                0.98  3.88    2.29  0.63
+    Flavanoids:                   0.34  5.08    2.03  1.00
+    Nonflavanoid Phenols:         0.13  0.66    0.36  0.12
+    Proanthocyanins:              0.41  3.58    1.59  0.57
+    Colour Intensity:              1.3  13.0     5.1   2.3
+    Hue:                          0.48  1.71    0.96  0.23
+    OD280/OD315 of diluted wines: 1.27  4.00    2.61  0.71
+    Proline:                       278  1680     746   315
+    ============================= ==== ===== ======= =====
+
+    :Missing Attribute Values: None
+    :Class Distribution: class_0 (59), class_1 (71), class_2 (48)
+    :Creator: R.A. Fisher
+    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
+    :Date: July, 1988
+
+This is a copy of UCI ML Wine recognition datasets.
+https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data
+
+The data is the results of a chemical analysis of wines grown in the same
+region in Italy by three different cultivators. There are thirteen different
+measurements taken for different constituents found in the three types of
+wine.
+
+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.
+
+Citation:
+
+Lichman, M. (2013). UCI Machine Learning Repository
+[https://archive.ics.uci.edu/ml]. Irvine, CA: University of California,
+School of Information and Computer Science. 
+
+.. topic:: 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% correct classification. 
+  (RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data)) 
+  (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).