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Add docs fro update_seq (#1735)

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* Fix typos and messages in docs

* parameter.md: Add docs for updater_seq

Mention the updater_seq parameter which sets the order of the tree
updaters to run and also specifies which ones to run. This can be
useful when pruning is not required or even a custom plugin is
being built along with xgboost.
This commit is contained in:
AbdealiJK 2016-11-05 04:37:29 +05:30 committed by Tianqi Chen
parent b94fcab4dc
commit 52b9867be5
2 changed files with 6 additions and 2 deletions
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6
doc/jvm/java_intro.md
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@ -3,7 +3,9 @@ XGBoost4J Java API
This tutorial introduces This tutorial introduces
## Data Interface ## Data Interface
Like the xgboost python module, xgboost4j use ```DMatrix``` to handle data, libsvm txt format file, sparse matrix in CSR/CSC format, and dense matrix is supported. Like the xgboost python module, xgboost4j uses ```DMatrix``` to handle data,
libsvm txt format file, sparse matrix in CSR/CSC format, and dense matrix is
supported.
* To import ```DMatrix``` : * To import ```DMatrix``` :
```java ```java
@ -97,7 +99,7 @@ import org.dmlc.xgboost4j.util.Trainer;
```java ```java
DMatrix trainMat = new DMatrix("train.svm.txt"); DMatrix trainMat = new DMatrix("train.svm.txt");
DMatrix validMat = new DMatrix("valid.svm.txt"); DMatrix validMat = new DMatrix("valid.svm.txt");
//specifiy a watchList to see the performance //specify a watchList to see the performance
//any Iterable<Entry<String, DMatrix>> object could be used as watchList //any Iterable<Entry<String, DMatrix>> object could be used as watchList
List<Entry<String, DMatrix>> watchs = new ArrayList<>(); List<Entry<String, DMatrix>> watchs = new ArrayList<>();
watchs.add(new SimpleEntry<>("train", trainMat)); watchs.add(new SimpleEntry<>("train", trainMat));

2
doc/parameter.md
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@ -73,6 +73,8 @@ Parameters for Tree Booster
- range: (0, 1) - range: (0, 1)
* scale_pos_weight, [default=1] * scale_pos_weight, [default=1]
- Control the balance of positive and negative weights, useful for unbalanced classes. A typical value to consider: sum(negative cases) / sum(positive cases) See [Parameters Tuning](how_to/param_tuning.md) for more discussion. Also see Higgs Kaggle competition demo for examples: [R](../demo/kaggle-higgs/higgs-train.R ), [py1](../demo/kaggle-higgs/higgs-numpy.py ), [py2](../demo/kaggle-higgs/higgs-cv.py ), [py3](../demo/guide-python/cross_validation.py) - Control the balance of positive and negative weights, useful for unbalanced classes. A typical value to consider: sum(negative cases) / sum(positive cases) See [Parameters Tuning](how_to/param_tuning.md) for more discussion. Also see Higgs Kaggle competition demo for examples: [R](../demo/kaggle-higgs/higgs-train.R ), [py1](../demo/kaggle-higgs/higgs-numpy.py ), [py2](../demo/kaggle-higgs/higgs-cv.py ), [py3](../demo/guide-python/cross_validation.py)
* updater_seq, [default="grow_colmaker,prune"]
- A comma separated string mentioning tThe sequence of Tree updaters that should be run. A tree updater is a pluggable operation performed on the tree at every step using the gradient information. Tree updaters can be registered using the plugin system provided.
Additional parameters for Dart Booster Additional parameters for Dart Booster
-------------------------------------- --------------------------------------