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Tianqi Chen 2015-05-10 17:45:20 -07:00
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@ -37,16 +37,15 @@ What's New
Features
========
* Sparse feature format:
- Sparse feature format allows easy handling of missing values, and improve computation efficiency.
* Push the limit on single machine:
- Efficient implementation that optimizes memory and computation.
* Speed: XGBoost is very fast
- 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
* Distributed and portable
- The distributed version of xgboost is highly portable and can be used in different platforms
- It inheritates all the optimizations made in single machine mode, maximumly utilize the resources using both multi-threading and distributed computing.
* Easily accessible in python, R, Julia, CLI
* Fast speed and memory efficient
- Can be more than 10 times faster than GBM in sklearn and R
- Handles sparse matrices, support external memory
* Accurate prediction, and used extensively by data scientists and kagglers
- See [highlight links](https://github.com/dmlc/xgboost/blob/master/doc/README.md#highlight-links)
* Distributed and Portable
- The distributed version runs on Hadoop (YARN), MPI, SGE etc.
- Scales to billions of examples and beyond
Build
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