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I have a bunch of methods that classify a binary outcome. I'm trying to figure out if some combination of those classifiers is better than any others.

I'm hoping to run a bunch of methods. I've thought of running random forests/cart and perhaps a SVM model.

What are some other common methods typically done for this?

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You're asking about whats known as 'ensembling'. I'd suggest reading about it here http://en.wikipedia.org/wiki/Ensemble_learning. Two popular methods are boosting and bagging.

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