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tqchen 2014-05-16 20:37:45 -07:00
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README.md
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@ -15,6 +15,8 @@ Features
- Sparse feature format allows easy handling of missing values, and improve computation efficiency. - Sparse feature format allows easy handling of missing values, and improve computation efficiency.
* 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
- IN [demo/higgs/speedtest.py](../blob/master/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

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demo/kaggle-higgs/README.md
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@ -14,8 +14,6 @@ make
3. Run ./run.sh 3. Run ./run.sh
Speed Speed
===== =====
speedtest.py compares xgboost's speed on this dataset with sklearn.GBM speedtest.py compares xgboost's speed on this dataset with sklearn.GBM