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.
• Python module now accepts different learning rates for each boosting round.
• Python now allows model training continuation from previously saved model.
• Additional parameters added for sklearn wrapper
• 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