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0899-7667
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1530-888X
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Neural Computation

January 2016, Vol. 28, No. 1, Pages 216-228
(doi: 10.1162/NECO_a_00793)
© 2015 Massachusetts Institute of Technology
An Empirical Overview of the No Free Lunch Theorem and Its Effect on Real-World Machine Learning Classification
Article PDF (773.53 KB)
Abstract

A sizable amount of research has been done to improve the mechanisms for knowledge extraction such as machine learning classification or regression. Quite unintuitively, the no free lunch (NFL) theorem states that all optimization problem strategies perform equally well when averaged over all possible problems. This fact seems to clash with the effort put forth toward better algorithms. This letter explores empirically the effect of the NFL theorem on some popular machine learning classification techniques over real-world data sets.