Hendrik/xgboost
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

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

Browse Source
This commit is contained in:
Philip Hyunsu Cho 2018-02-09 11:16:44 -08:00 committed by GitHub
parent 81d1b17f9c
commit 375d75304d
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 2 additions and 2 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
doc/parameter.md
View File

@ -96,7 +96,7 @@ Parameters for Tree Booster
- A type of boosting process to run.
- Choices: {'default', 'update'}
- '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']
- Controls a way new nodes are added to the tree.
- Currently supported only if `tree_method` is set to 'hist'.

2
doc/tutorials/dart.md
View File

@ -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.
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`.