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Tianqi Chen 2014-05-16 20:41:43 -07:00
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@ -16,7 +16,7 @@ Features
* Push the limit on single machine: * Push the limit on single machine:
- Efficient implementation that optimizes memory and computation. - Efficient implementation that optimizes memory and computation.
* Speed: XGBoost is very fast * Speed: XGBoost is very fast
- IN [demo/higgs/speedtest.py](../demo/master/kaggle-higgs/speedtest.py), kaggle higgs data it is faster(on our machine 20 times faster using 4 threads) than sklearn.ensemble.GradientBoostingClassifier - IN [demo/higgs/speedtest.py](../demo/kaggle-higgs/speedtest.py), kaggle higgs data it is faster(on our machine 20 times faster using 4 threads) than sklearn.ensemble.GradientBoostingClassifier
* Layout of gradient boosting algorithm to support user defined objective * Layout of gradient boosting algorithm to support user defined objective
* Python interface, works with numpy and scipy.sparse matrix * Python interface, works with numpy and scipy.sparse matrix