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README.md

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+## Breast Cancer Wisconsin Diagnostic Dataset
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+Following description was retrieved from [breast cancer dataset on UCI machine learning repository](https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(diagnostic)).
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+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. A few of the images can be found at [here](https://pages.cs.wisc.edu/~street/images/).
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+Separating plane described above was obtained using Multisurface Method-Tree (MSM-T), 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.
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+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].
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+Attribute Information:
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+- ID number
+- Diagnosis (M = malignant, B = benign)
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+Ten real-valued features are computed for each cell nucleus:
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+- 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)
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