53 lines
1.9 KiB
Markdown
53 lines
1.9 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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* Fix List
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- Fixed possible problem of poisson regression for R.
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* Python module now throw exception instead of crash terminal when a parameter error happens.
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* Python module now has importance plot and tree plot functions.
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* Python module now accepts different learning rates for each boosting round.
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* Python module now allows model training continuation from previously saved model.
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* Python module now allows early stopping in CV.
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* Additional parameters added for sklearn wrapper
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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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* Improvements in sklearn compatible module
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* Added pip installation functionality for python module
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* Switch from 0 to NA for missing values in R
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