On Overfitting Avoidance as Bias

David H. Wolpert

Working Papers from Santa Fe Institute

Abstract: In supervising learning it is commonly believed that penalizing complex functions help one avoid ``overfitting'' functions to data, and therefore improves generalization. It is also commonly believed that cross-validation is an effective way to choose amongst algorithms for fitting functions to data. In a recent paper, Schaffer (1993) presents experimental evidence disputing these claims. The current paper consists of a formal analysis of these contentions of Schaffer's. It proves that his contentions are valid, although some of his experiments must be interpreted with caution.

Date: 1993-03
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Persistent link: https://EconPapers.repec.org/RePEc:wop:safiwp:93-03-016

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