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Ensemble Methods in Data Mining by John Elder, Giovanni Seni

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CHAPTER 1

Ensembles Discovered

…and in a multitude of counselors there is safety.

Proverbs 24:6b

A wide variety of competing methods are available for inducing models from data, and their relative strengths are of keen interest. The comparative accuracy of popular algorithms depends strongly on the details of the problems addressed, as shown in Figure 1.1 (from Elder and Lee (1997)), which plots the relative out-of-sample error of five algorithms for six public-domain problems. Overall, neural network models did the best on this set of problems, but note that every algorithm scored best or next-to-best on at least two of the six data sets.

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