Technical Note: Selecting a Classification Method by Cross-Validation |
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Authors: | Schaffer Cullen |
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Affiliation: | (1) Department of Computer Science, CUNY/Hunter College, 695 Park Avenue, 10021 New York, NY |
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Abstract: | If we lack relevant problem-specific knowledge, cross-validation methods may be used to select a classification method empirically. We examine this idea here to show in what senses cross-validation does and does not solve the selection problem. As illustrated empirically, cross-validation may lead to higher average performance than application of any single classification strategy, and it also cuts the risk of poor performance. On the other hand, cross-validation is no more or less a form of bias than simpler strategies, and applying it appropriately ultimately depends in the same way on prior knowledge. In fact, cross-validation may be seen as a way of applying partial information about the applicability of alternative classification strategies. |
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Keywords: | Cross-validation classification decision trees neural networks |
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