Wisconsin Diagnostic Breast Cancer Dataset

The WDBC dataset contains quantitative measurements from digitized images of fine needle aspirates (FNA) of breast masses. It is commonly used for classification tasks to distinguish between benign and malignant tumors.

wdbc

Format

A data frame with 569 rows and 32 columns:

radius_mean: Mean of radius
radius_se: Standard error of radius
radius_worst: Worst (largest) radius
texture_mean: Mean of texture
texture_se: Standard error of texture
texture_worst: Worst texture
perimeter_mean: Mean of perimeter
perimeter_se: Standard error of perimeter
perimeter_worst: Worst perimeter
area_mean: Mean area
area_se: Standard error of area
area_worst: Worst area
smoothness_mean: Mean smoothness
smoothness_se: Standard error of smoothness
smoothness_worst: Worst smoothness
compactness_mean: Mean compactness
compactness_se: Standard error of compactness
compactness_worst: Worst compactness
concavity_mean: Mean concavity
concavity_se: Standard error of concavity
concavity_worst: Worst concavity
concave.points_mean: Mean concave points
concave.points_se: Standard error of concave points
concave.points_worst: Worst concave points
symmetry_mean: Mean symmetry
symmetry_se: Standard error of symmetry
symmetry_worst: Worst symmetry
fractal_dimension_mean: Mean fractal dimension
fractal_dimension_se: Standard error of fractal dimension
fractal_dimension_worst: Worst fractal dimension
diagnosis: Factor with levels 'B' and 'M'

Source

Dr. William H. Wolberg, W. Nick Street, and Olvi L. Mangasarian, University of Wisconsin<U+2013>Madison. Original dataset available at: <https://archive.ics.uci.edu/dataset/17/breast+cancer+wisconsin+diagnostic>