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I thought it would be interesting to talk about two of the best ensemble methods off-the-shelf: Random Forests and Boosting.

  • When would you apply one method rather than the other one?
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I would use whichever one performed better out of sample.

So far, I've found in impossible to tell which model will be better for a novel problem a priori.

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Just to start, a quick thought.

Random Forest can run in parallel and they are much faster to train, Boosting is an iterative algorithm instead. However, Boosting might converge early iteration-wise.

Boosting might overfit when there are many noisy features but Random Forest also does.

On the other hand, their target is almost similar: produce many different weak learners as much different as possible from each others. Random Forests tackle the problem with randomization and Boosting focuses on mis-classified examples of previous models to build a different one.

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  • $\begingroup$ Random forest doesn't try to produce many "different weak" learners. The learners are random but not weak in the sense that they are just slightly better than random guess. $\endgroup$ – SmallChess Sep 17 '16 at 0:31

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