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

on going at master

Fix List
- Fixed possible problem of poisson regression for R.
Python module now throw exception instead of crash terminal when a parameter error happens.
Python module now has importance plot and tree plot functions.
Java api is ready for use
Added more test cases and continuous integration to make each build more robust
Improvements in sklearn compatible module
Added pip installation functionality for python module
Switch from 0 to NA for missing values in R