The data set provides data for 569 patients on 30 features of the cell nuclei obtained from a digitized image of a fine needle aspirate (FNA) of a breast mass. For each patient the cancer was diagnosed as malignant or benign.

`data(wdbc)`

A data frame with 569 observations on the following variables:

`ID`

ID number

`Diagnosis`

cancer diagnosis:

`M`

= malignant,`B`

= benign`Radius_mean`

a numeric vector

`Texture_mean`

a numeric vector

`Perimeter_mean`

a numeric vector

`Area_mean`

a numeric vector

`Smoothness_mean`

a numeric vector

`Compactness_mean`

a numeric vector

`Concavity_mean`

a numeric vector

`Nconcave_mean`

a numeric vector

`Symmetry_mean`

a numeric vector

`Fractaldim_mean`

a numeric vector

`Radius_se`

a numeric vector

`Texture_se`

a numeric vector

`Perimeter_se`

a numeric vector

`Area_se`

a numeric vector

`Smoothness_se`

a numeric vector

`Compactness_se`

a numeric vector

`Concavity_se`

a numeric vector

`Nconcave_se`

a numeric vector

`Symmetry_se`

a numeric vector

`Fractaldim_se`

a numeric vector

`Radius_extreme`

a numeric vector

`Texture_extreme`

a numeric vector

`Perimeter_extreme`

a numeric vector

`Area_extreme`

a numeric vector

`Smoothness_extreme`

a numeric vector

`Compactness_extreme`

a numeric vector

`Concavity_extreme`

a numeric vector

`Nconcave_extreme`

a numeric vector

`Symmetry_extreme`

a numeric vector

`Fractaldim_extreme`

a numeric vector

The recorded features are:

`Radius`

as mean of distances from center to points on the perimeter`Texture`

as standard deviation of gray-scale values`Perimeter`

as cell nucleus perimeter`Area`

as cell nucleus area`Smoothness`

as local variation in radius lengths`Compactness`

as cell nucleus compactness, perimeter^2 / area - 1`Concavity`

as severity of concave portions of the contour`Nconcave`

as number of concave portions of the contour`Symmetry`

as cell nucleus shape`Fractaldim`

as fractal dimension, "coastline approximation" - 1

For each feature the recorded values are computed from each image as `<feature_name>_mean`

, `<feature_name>_se`

, and `<feature_name>_extreme`

, for the mean, the standard error, and the mean of the three largest values.

Mangasarian, O. L., Street, W. N., and Wolberg, W. H. (1995) Breast cancer diagnosis and prognosis via linear programming. *Operations Research*, 43(4), pp. 570-577.