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Results tagged with bagging
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user 221761
Bagging or bootstrap aggregation is a special case of model averaging. Given a standard training set bagging generates $m$ new training sets by bootstrapping, and then the results of using some training method on the $m$ generated data sets are averaged. Bagging can stabilize results from some unstable methods such as 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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