The Wisconsin Breast Cancer Diagnostic Data Set is a set of data used to classify breast lumps as malignant or benign based on the values of 30 potential predictors, obtained by measuring the nuclei of fluid removed using a fine needle aspirate. This paper demonstrates how one might best visualize this data and then how to fit four classification models using logistic modeling, recursive partitioning and neural nets.
Classification of Breast Cancer Cells Using JMP®
