{
   "name": "wdbc",
   "title": "Wdbc",
   "resources": [
      {
         "path": "wdbc.arff",
         "pathType": "local",
         "name": "wdbc",
         "format": "arff",
         "encoding": "ISO-8859-1"
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      {
         "path": "wdbc.csv",
         "pathType": "local",
         "name": "wdbc",
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         "mediatype": "text/csv",
         "encoding": "ISO-8859-2",
         "dialect": {
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            "quoteChar": "\""
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                  "type": "number",
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                  "type": "number",
                  "format": "default"
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                  "format": "default"
               },
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   ],
   "readme": "The resources for this dataset can be found at https://www.openml.org/d/1510\n\nAuthor: William H. Wolberg, W. Nick Street, Olvi L. Mangasarian    \nSource: [UCI](https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(original)), [University of Wisconsin](http://pages.cs.wisc.edu/~olvi/uwmp/cancer.html) - 1995  \nPlease cite: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html)     \n\nBreast Cancer Wisconsin (Diagnostic) Data Set (WDBC). 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. The target feature records the prognosis (benign (1) or malignant (2)). [Original data available here](ftp://ftp.cs.wisc.edu/math-prog/cpo-dataset/machine-learn/cancer/) \n\nCurrent dataset was adapted to ARFF format from the UCI version. Sample code ID's were removed.  \n\n! Note that there is also a related Breast Cancer Wisconsin (Original) Data Set with a different set of features, better known as [breast-w](https://www.openml.org/d/15).\n\n\n### Feature description  \n\nTen real-valued features are computed for each of 3 cell nuclei, yielding a total of 30 descriptive features. See the papers below for more details on how they were computed. The 10 features (in order) are:  \n\na) radius (mean of distances from center to points on the perimeter)  \nb) texture (standard deviation of gray-scale values)  \nc) perimeter  \nd) area  \ne) smoothness (local variation in radius lengths)  \nf) compactness (perimeter^2 / area - 1.0)  \ng) concavity (severity of concave portions of the contour)  \nh) concave points (number of concave portions of the contour)  \ni) symmetry  \nj) fractal dimension (\"coastline approximation\" - 1)  \n\n### Relevant Papers   \n\nW.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. \n\nO.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.",
   "description": "The resources for this dataset can be found at https://www.openml.org/d/1510\n\nAuthor: William H. Wol",
   "licenses": [
      {
         "name": "ODC-PDDL",
         "path": "http://opendatacommons.org/licenses/pddl/",
         "title": "Open Data Commons Public Domain Dedication and License"
      }
   ]
}