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Tianqi Chen 2015-04-19 14:21:12 -07:00
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Binary Classification
====
This is the quick start tutorial for xgboost CLI version. You can also checkout [../../doc/README.md](../../doc/README.md) for links to tutorial in pyton or R.
This is the quick start tutorial for xgboost CLI version. You can also checkout [../../doc/README.md](../../doc/README.md) for links to tutorial in python or R.
Here we demonstrate how to use XGBoost for a binary classification task. Before getting started, make sure you compile xgboost in the root directory of the project by typing ```make```
@ -61,10 +61,10 @@ eval[test] = "agaricus.txt.test"
# The path of test data
test:data = "agaricus.txt.test"
```
We use the [tree booster](https://github.com/tqchen/xgboost/wiki/Tree-Booster) and logistic regression objective in our setting. This indicates that we accomplish our task using classic gradient boosting regression tree(GBRT), which is a promising method for binary classification.
We use the tree booster and logistic regression objective in our setting. This indicates that we accomplish our task using classic gradient boosting regression tree(GBRT), which is a promising method for binary classification.
The parameters shown in the example gives the most common ones that are needed to use xgboost.
If you are interested in more parameter settings, the complete parameter settings and detailed descriptions are [here](https://github.com/tqchen/xgboost/wiki/Parameters). Besides putting the parameters in the configuration file, we can set them by passing them as arguments as below:
If you are interested in more parameter settings, the complete parameter settings and detailed descriptions are [here](../../doc/parameter.md). Besides putting the parameters in the configuration file, we can set them by passing them as arguments as below:
```
../../xgboost mushroom.conf max_depth=6