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+.. _breast_cancer_dataset:
+
+Breast cancer wisconsin (diagnostic) dataset
+--------------------------------------------
+
+**Data Set Characteristics:**
+
+    :Number of Instances: 569
+
+    :Number of Attributes: 30 numeric, predictive attributes and the class
+
+    :Attribute Information:
+        - radius (mean of distances from center to points on the perimeter)
+        - texture (standard deviation of gray-scale values)
+        - perimeter
+        - area
+        - smoothness (local variation in radius lengths)
+        - compactness (perimeter^2 / area - 1.0)
+        - concavity (severity of concave portions of the contour)
+        - concave points (number of concave portions of the contour)
+        - symmetry
+        - fractal dimension ("coastline approximation" - 1)
+
+        The mean, standard error, and "worst" or largest (mean of the three
+        worst/largest values) of these features were computed for each image,
+        resulting in 30 features.  For instance, field 0 is Mean Radius, field
+        10 is Radius SE, field 20 is Worst Radius.
+
+        - class:
+                - WDBC-Malignant
+                - WDBC-Benign
+
+    :Summary Statistics:
+
+    ===================================== ====== ======
+                                           Min    Max
+    ===================================== ====== ======
+    radius (mean):                        6.981  28.11
+    texture (mean):                       9.71   39.28
+    perimeter (mean):                     43.79  188.5
+    area (mean):                          143.5  2501.0
+    smoothness (mean):                    0.053  0.163
+    compactness (mean):                   0.019  0.345
+    concavity (mean):                     0.0    0.427
+    concave points (mean):                0.0    0.201
+    symmetry (mean):                      0.106  0.304
+    fractal dimension (mean):             0.05   0.097
+    radius (standard error):              0.112  2.873
+    texture (standard error):             0.36   4.885
+    perimeter (standard error):           0.757  21.98
+    area (standard error):                6.802  542.2
+    smoothness (standard error):          0.002  0.031
+    compactness (standard error):         0.002  0.135
+    concavity (standard error):           0.0    0.396
+    concave points (standard error):      0.0    0.053
+    symmetry (standard error):            0.008  0.079
+    fractal dimension (standard error):   0.001  0.03
+    radius (worst):                       7.93   36.04
+    texture (worst):                      12.02  49.54
+    perimeter (worst):                    50.41  251.2
+    area (worst):                         185.2  4254.0
+    smoothness (worst):                   0.071  0.223
+    compactness (worst):                  0.027  1.058
+    concavity (worst):                    0.0    1.252
+    concave points (worst):               0.0    0.291
+    symmetry (worst):                     0.156  0.664
+    fractal dimension (worst):            0.055  0.208
+    ===================================== ====== ======
+
+    :Missing Attribute Values: None
+
+    :Class Distribution: 212 - Malignant, 357 - Benign
+
+    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian
+
+    :Donor: Nick Street
+
+    :Date: November, 1995
+
+This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.
+https://goo.gl/U2Uwz2
+
+Features are computed from a digitized image of a fine needle
+aspirate (FNA) of a breast mass.  They describe
+characteristics of the cell nuclei present in the image.
+
+Separating plane described above was obtained using
+Multisurface Method-Tree (MSM-T) [K. P. Bennett, "Decision Tree
+Construction Via Linear Programming." Proceedings of the 4th
+Midwest Artificial Intelligence and Cognitive Science Society,
+pp. 97-101, 1992], a classification method which uses linear
+programming to construct a decision tree.  Relevant features
+were selected using an exhaustive search in the space of 1-4
+features and 1-3 separating planes.
+
+The actual linear program used to obtain the separating plane
+in the 3-dimensional space is that described in:
+[K. P. Bennett and O. L. Mangasarian: "Robust Linear
+Programming Discrimination of Two Linearly Inseparable Sets",
+Optimization Methods and Software 1, 1992, 23-34].
+
+This database is also available through the UW CS ftp server:
+
+ftp ftp.cs.wisc.edu
+cd math-prog/cpo-dataset/machine-learn/WDBC/
+
+.. topic:: References
+
+   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction 
+     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on 
+     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,
+     San Jose, CA, 1993.
+   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and 
+     prognosis via linear programming. Operations Research, 43(4), pages 570-577, 
+     July-August 1995.
+   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques
+     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) 
+     163-171.