437b368b1f
This bring many goodies, including:
* Ability to specify delimiter and weight_column for CSV files:
```python
dtrain = xgboost.DMatrix('train.csv?format=csv&label_column=0&weight_column=1&delimiter= ')
```
* Ability to choose between 0-based and 1-based indexing for LIBSVM/LIBFM files:
```python
dtrain = xgboost.DMatrix('train.libsvm?indexing_mode=1') # use 1-based indexing
dtest = xgboost.DMatrix('test.libsvm') # use 0-based indexing (default)
dtest2 = xgboost.DMatrix('test2.libsvm?indexing_mode=-1') # use heuristic to detect 0-based / 1-based
```
* Fix a bug in float parsing (issue dmlc/dmlc-core#440)
eXtreme Gradient Boosting
Community | Documentation | Resources | Contributors | Release Notes
XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. The same code runs on major distributed environment (Hadoop, SGE, MPI) and can solve problems beyond billions of examples.
License
© Contributors, 2016. Licensed under an Apache-2 license.
Contribute to XGBoost
XGBoost has been developed and used by a group of active community members. Your help is very valuable to make the package better for everyone. Checkout the Community Page
Reference
- Tianqi Chen and Carlos Guestrin. XGBoost: A Scalable Tree Boosting System. In 22nd SIGKDD Conference on Knowledge Discovery and Data Mining, 2016
- XGBoost originates from research project at University of Washington.