𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Selecting the right-size model for prediction

✍ Scribed by Sholom M. Weiss; Nitin Indurkhya

Book ID
104634695
Publisher
Springer US
Year
1996
Tongue
English
Weight
1011 KB
Volume
6
Category
Article
ISSN
0924-669X

No coin nor oath required. For personal study only.

✦ Synopsis

We evaluate the effectiveness of cross-validation in selecting the right-size model for decision tree and k-nearest neighbor learning methods. For samples with at least 200 cases, extensive empirical evidence supports the following conclusions relative to complexity-fit selection: (a) IO-fold cross-validation is nearly unbiased; (b) ignoring model complexity-fit and picking the "standard" model is highly biased; (c) IO-fold cross-validation is consistent with optimal complexity-fit selection for large sample sizes and (d) the accuracy of complexity-fit selection by IO-fold cross-validation is largely dependent on sample size, irrespective of the population distribution.

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