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Ensemble Methods in Data Mining by John Elder, Giovanni Seni

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CHAPTER 4

Importance Sampling and the Classic Ensemble Methods

In this chapter, we provide an overview of Importance Sampling Learning Ensembles (ISLE) (Friedman and Popescu, 2003). The ISLE framework allows us to view the classic ensemble methods of Bagging, Random Forest, AdaBoost, and Gradient Boosting as special cases of a single algorithm. This unified view clarifies the properties of these methods and suggests ways to improve their accuracy and speed.

The type of ensemble models we are discussing here can be described as an additive expansion of the form:

image

where the are known as basis functions or also called base learners. For example, ...

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