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Predicting if the cancer diagnosis is benign or malignant based on several observations/features
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30 features are used, examples: - 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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Datasets are linearly separable using all 30 input features
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Number of Instances: 569
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Class Distribution: 212 Malignant, 357 Benign
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Target class: - Malignant - Benign
https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)
