2 Homogeneous parallel ensembles: Bagging and random forests
This chapter covers
- Training homogeneous parallel ensembles
- Implementing and understanding bagging
- Implementing and understanding how random forests work
- Training variants with pasting, random subspaces, random patches, and Extra Trees
- Using bagging and random forests in practice
In chapter 1, we introduced ensemble learning and created our first rudimentary ensemble. To recap, an ensemble method relies on the notion of “wisdom of the crowd”: the combined answer of many models is often better than any one individual answer. We begin our journey into ensemble learning methods in earnest with parallel ensemble methods. We begin with this type of ensemble method because, conceptually, ...
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