Change Log

xgboost-0.1

Initial release

xgboost-0.2x

Python module
Weighted samples instances
Initial version of pairwise rank

xgboost-0.3

Faster tree construction module
- Allows subsample columns during tree construction via bst:col_samplebytree=ratio
Support for boosting from initial predictions
Experimental version of LambdaRank
Linear booster is now parallelized, using parallel coordinated descent.
Add Code Guide for customizing objective function and evaluation
Add R module

xgboost-0.4

Distributed version of xgboost that runs on YARN, scales to billions of examples
Direct save/load data and model from/to S3 and HDFS
Feature importance visualization in R module, by Michael Benesty
Predict leaf index
Poisson regression for counts data
Early stopping option in training
Native save load support in R and python
- xgboost models now can be saved using save/load in R
- xgboost python model is now pickable
sklearn wrapper is supported in python module
Experimental External memory version