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A family of algorithms combining weakly predictive models into a strongly predictive model. The most common approach is called gradient boosting, and the most commonly used weak models are classification/regression trees.

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why does random forest trees need to be deeper than gradient boosting trees

in Elements of Statistical Learning chapter 15. Random Forest, we see authors' note on RF v.s. GBT. One of them is that at 1000 terms, GBM depth 4 has smaller error than RF depth 6. Also we notice RF …
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