Merge branch 'master' of ssh://github.com/tqchen/xgboost

README.md

@@ -14,6 +14,8 @@ Notes on the Code: [Code Guide](src)
 
 What's New
 =====
+
+* Thanks to Bing Xu, [XGBoost.jl](https://github.com/antinucleon/XGBoost.jl) allows you to use xgboost from Julia
 * See the updated [demo folder](demo) for feature walkthrough
 * Thanks to Tong He, the new [R package](R-package) is available
 
@@ -26,7 +28,6 @@ Features
 * 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
-* Python interface, works with numpy and scipy.sparse matrix
 
 Build
 =====

demo/README.md

@@ -8,12 +8,30 @@ This folder contains the all example codes using xgboost.
 Features Walkthrough
 ====
 This is a list of short codes introducing different functionalities of xgboost and its wrapper.
-* Basic walkthrough of wrappers [python](guide-python/basic_walkthrough.py)
+* Basic walkthrough of wrappers 
-* Cutomize loss function, and evaluation metric [python](guide-python/custom_objective.py)
+  [python](guide-python/basic_walkthrough.py)
-* Boosting from existing prediction [python](guide-python/boost_from_prediction.py)
+  [R](../R-package/demo/basic_walkthrough.R)
-* Predicting using first n trees [python](guide-python/predict_first_ntree.py)
+  [Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/basic_walkthrough.jl)
-* Generalized Linear Model [python](guide-python/generalized_linear_model.py)
+* Cutomize loss function, and evaluation metric
-* Cross validation [python](guide-python/cross_validation.py)
+  [python](guide-python/custom_objective.py)
+  [R](../R-package/demo/custom_objective.R)
+  [Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/custom_objective.jl)
+* Boosting from existing prediction
+  [python](guide-python/boost_from_prediction.py)
+  [R](../R-package/demo/boost_from_prediction.R)
+  [Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/boost_from_prediction.jl)
+* Predicting using first n trees 
+  [python](guide-python/predict_first_ntree.py)
+  [R](../R-package/demo/boost_from_prediction.R)
+  [Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/boost_from_prediction.jl)  
+* Generalized Linear Model
+  [python](guide-python/generalized_linear_model.py)
+  [R](../R-package/demo/generalized_linear_model.R)
+  [Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/generalized_linear_model.jl)  
+* Cross validation
+  [python](guide-python/cross_validation.py)
+  [R](../R-package/demo/cross_validation.R)
+  [Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/cross_validation.jl)  
 
 Basic Examples by Tasks
 ====

demo/kaggle-higgs/README.md

@@ -1,3 +1,9 @@
+Highlights
+=====
+Higgs challenge ends recently, xgboost is being used by many users. This list highlights the xgboost solutions of players
+* Blogpost by phunther: [Winning solution of Kaggle Higgs competition: what a single model can do](http://no2147483647.wordpress.com/2014/09/17/winning-solution-of-kaggle-higgs-competition-what-a-single-model-can-do/)
+
+
 Guide for Kaggle Higgs Challenge
 =====
 

wrapper/README.md

@@ -10,3 +10,7 @@ Python
 R
 =====
 * See [R-package](../R-package)
+
+Julia
+=====
+* See [XGBoost.jl](https://github.com/antinucleon/XGBoost.jl)