58 lines
2.0 KiB
Markdown
58 lines
2.0 KiB
Markdown
Change Log
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==========
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xgboost-0.1
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===========
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* Initial release
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xgboost-0.2x
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============
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* Python module
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* Weighted samples instances
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* Initial version of pairwise rank
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xgboost-0.3
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===========
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* Faster tree construction module
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- Allows subsample columns during tree construction via ```bst:col_samplebytree=ratio```
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* Support for boosting from initial predictions
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* Experimental version of LambdaRank
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* Linear booster is now parallelized, using parallel coordinated descent.
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* Add [Code Guide](src/README.md) for customizing objective function and evaluation
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* Add R module
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xgboost-0.4
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===========
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* Distributed version of xgboost that runs on YARN, scales to billions of examples
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* Direct save/load data and model from/to S3 and HDFS
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* Feature importance visualization in R module, by Michael Benesty
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* Predict leaf index
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* Poisson regression for counts data
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* Early stopping option in training
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* Native save load support in R and python
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- xgboost models now can be saved using save/load in R
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- xgboost python model is now pickable
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* sklearn wrapper is supported in python module
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* Experimental External memory version
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on going at master
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==================
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* Changes in R library
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- fixed possible problem of poisson regression.
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- switched from 0 to NA for missing values.
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* Changes in Python library
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- throws exception instead of crash terminal when a parameter error happens.
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- has importance plot and tree plot functions.
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- accepts different learning rates for each boosting round.
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- allows model training continuation from previously saved model.
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- allows early stopping in CV.
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- allows feval to return a list of tuples.
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- allows eval_metric to handle additional format.
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- improved compatibility in sklearn module.
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- additional parameters added for sklearn wrapper.
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- added pip installation functionality.
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- supports more Pandas DataFrame dtypes.
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- added best_ntree_limit attribute, in addition to best_score and best_iteration.
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* Java api is ready for use
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* Added more test cases and continuous integration to make each build more robust.
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