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On Overfitting Avoidance as Bias

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  • David H. Wolpert

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.

Suggested Citation

  • David H. Wolpert, 1993. "On Overfitting Avoidance as Bias," Working Papers 93-03-016, Santa Fe Institute.
    • Handle: RePEc:wop:safiwp:93-03-016
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