diff --git a/CHANGES.md b/CHANGES.md new file mode 100644 index 000000000..ee2adbbb7 --- /dev/null +++ b/CHANGES.md @@ -0,0 +1,21 @@ +Change Log of Versions +===== + +xgboost-0.1 +===== +* Initial release + +xgboost-0.2x +===== +* Python module +* Weighted samples instances +* Initial version of pairwise rank + +xgboost-unity +===== +* Faster tree construction module + - Allows subsample columns as well during tree construction +* Support for boosting from initial predictions +* Experimental version of LambdaRank +* Linear booster is now parallelized, using parallel coordinated descent. +* Add [code guide](src/README.md) diff --git a/README.md b/README.md index e226aec94..f9a72e418 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,5 @@ xgboost: eXtreme Gradient Boosting -======= +====== An optimized general purpose gradient boosting (tree) library. Contributors: https://github.com/tqchen/xgboost/graphs/contributors @@ -8,8 +8,10 @@ Turorial and Documentation: https://github.com/tqchen/xgboost/wiki Questions and Issues: [https://github.com/tqchen/xgboost/issues](https://github.com/tqchen/xgboost/issues?q=is%3Aissue+label%3Aquestion) +Notes on the Code: [src/REAMDE.md](src/README.md) + Features -======= +====== * Sparse feature format: - Sparse feature format allows easy handling of missing values, and improve computation efficiency. * Push the limit on single machine: @@ -19,11 +21,12 @@ Features * Layout of gradient boosting algorithm to support user defined objective * Python interface, works with numpy and scipy.sparse matrix -xgboost-unity -======= -* Experimental branch(not usable yet): refactor xgboost, cleaner code, more flexibility +Version +====== +* This version is named xgboost-unity, the code has been refactored from 0.2x to be cleaner and more flexibility * This version of xgboost is not compatible with 0.2x, due to huge amount of changes in code structure - This means the model and buffer file of previous version can not be loaded in xgboost-unity +* For legacy 0.2x code, refer to Build ====== @@ -35,3 +38,4 @@ Build * Possible way to build using Visual Studio (not tested): - In principle, you can put src/xgboost.cpp and src/io/io.cpp into the project, and build xgboost. - For python module, you need python/xgboost_wrapper.cpp and src/io/io.cpp to build a dll. +