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Ensemble Methods in Data Mining by John Elder, Giovanni Seni

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CHAPTER 5

Rule Ensembles and Interpretation Statistics

Ensemble methods perform extremely well in a variety of problem domains, have desirable statistical properties, and scale well computationally. A classic ensemble model, however, is not as easy to interpret as a simple decision tree. In this chapter, we provide an overview of Rule Ensembles (Friedman and Popescu, 2005; Friedman and Bogdan, 2008), a new ISLE-based model built by combining simple, readable rules. While maintaining (and often improving) the accuracy of the classic tree ensemble, the rule-based model is much more interpretable. In this chapter, we will also illustrate recently proposed interpretation statistics which are applicable to Rule Ensembles as well as to most other ...

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