Change Log

xgboost-0.1

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

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

Changes in R library
- fixed possible problem of poisson regression.
- switched from 0 to NA for missing values.
- exposed access to additional model parameters.
Changes in Python library
- throws exception instead of crash terminal when a parameter error happens.
- has importance plot and tree plot functions.
- accepts different learning rates for each boosting round.
- allows model training continuation from previously saved model.
- allows early stopping in CV.
- allows feval to return a list of tuples.
- allows eval_metric to handle additional format.
- improved compatibility in sklearn module.
- additional parameters added for sklearn wrapper.
- added pip installation functionality.
- supports more Pandas DataFrame dtypes.
- added best_ntree_limit attribute, in addition to best_score and best_iteration.
Java api is ready for use
Added more test cases and continuous integration to make each build more robust.