This article describes discriminant analysis. First, a brief overview is provided followed by exploratory data analysis, discussions of allocation rules and their error rates, Bayes allocation rules, discrimination using normal models, nonparametric discrimination, logistic discriminant analysis, discrimination using artificial networks, tree‐based models, further methods of discrimination, and judging reliability. Finally, a section on software packages concludes the article. Numerous examples from medical applications support the understanding of the procedures presented.
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