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We investigate the relative efficacy of several classification models with and without feature selection. Simple classification rules are frequently preferable and superior to more complex models for microarray data that are typically undersampled. Improved classification accuracy is obtained with feature selection. We summarize some of the important questions considered in the literature that practitioners have to take into account when selecting a classifier for microarrays.