add log

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)

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.
+