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Fix typos, addressing issues #2212 and #3090 (#3105)

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Philip Hyunsu Cho 2018-02-09 11:16:44 -08:00 committed by GitHub
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doc/parameter.md
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@ -96,7 +96,7 @@ Parameters for Tree Booster
- A type of boosting process to run. - A type of boosting process to run.
- Choices: {'default', 'update'} - Choices: {'default', 'update'}
- 'default': the normal boosting process which creates new trees. - 'default': the normal boosting process which creates new trees.
- 'update': starts from an existing model and only updates its trees. In each boosting iteration, a tree from the initial model is taken, a specified sequence of updater plugins is run for that tree, and a modified tree is added to the new model. The new model would have either the same or smaller number of trees, depending on the number of boosting iteratons performed. Currently, the following built-in updater plugins could be meaningfully used with this process type: 'refresh', 'prune'. With 'update', one cannot use updater plugins that create new nrees. - 'update': starts from an existing model and only updates its trees. In each boosting iteration, a tree from the initial model is taken, a specified sequence of updater plugins is run for that tree, and a modified tree is added to the new model. The new model would have either the same or smaller number of trees, depending on the number of boosting iteratons performed. Currently, the following built-in updater plugins could be meaningfully used with this process type: 'refresh', 'prune'. With 'update', one cannot use updater plugins that create new trees.
* grow_policy, string [default='depthwise'] * grow_policy, string [default='depthwise']
- Controls a way new nodes are added to the tree. - Controls a way new nodes are added to the tree.
- Currently supported only if `tree_method` is set to 'hist'. - Currently supported only if `tree_method` is set to 'hist'.

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doc/tutorials/dart.md
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@ -3,7 +3,7 @@ DART booster
[XGBoost](https://github.com/dmlc/xgboost)) mostly combines a huge number of regression trees with a small learning rate. [XGBoost](https://github.com/dmlc/xgboost)) mostly combines a huge number of regression trees with a small learning rate.
In this situation, trees added early are significant and trees added late are unimportant. In this situation, trees added early are significant and trees added late are unimportant.
Rasmi et al. proposed a new method to add dropout techniques from the deep neural net community to boosted trees, and reported better results in some situations. 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.
This is a instruction of new tree booster `dart`. This is a instruction of new tree booster `dart`.