Distance‐weighted discrimination is a classification (discrimination) method. Like the popular support vector machine, it is rooted in optimization; however, the underlying optimization problem is modified to give better generalizability, particularly in high dimensions. The two key ideas are that distance‐weighted discrimination directly targets the data piling problem and also correctly handles unknown, unbalanced subclasses in the data. A useful property of distance‐weighted discrimination, beyond just good classification performance, is that it provides a direction vector in high‐dimensional data space with several purposes, including indication of driving phenomena behind class differences, data visualization, and batch adjustment tasks.WIREs Comput Stat2015, 7:109–114. doi: 10.1002/wics.1345This article is categorized under:Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and ClassificationApplications of Computational Statistics > Genomics/Proteomics/GeneticsStatistical and Graphical Methods of Data Analysis > Statistical Graphics and Visualization