Minor: removed extra parenthesis in doc (#3119)
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DART booster
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[XGBoost](https://github.com/dmlc/xgboost)) mostly combines a huge number of regression trees with a small learning rate.
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[XGBoost](https://github.com/dmlc/xgboost) mostly combines a huge number of regression trees with a small learning rate.
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In this situation, trees added early are significant and trees added late are unimportant.
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Vinayak and Gilad-Bachrach proposed a new method to add dropout techniques from the deep neural net community to boosted trees, and reported better results in some situations.
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