XGBoost Change Log

This file records the changes in xgboost library in reverse chronological order.

brick: next release candidate

Major refactor of core library.
- Goal: more flexible and modular code as a portable library.
- Switch to use of c++11 standard code.
- Random number generator defaults to std::mt19937.
- Share the data loading pipeline and logging module from dmlc-core.
- Enable registry pattern to allow optionally plugin of objective, metric, tree constructor, data loader.
  - Future plugin modules can be put into xgboost/plugin and register back to the library.
- Remove most of the raw pointers to smart ptrs, for RAII safety.
Change library name to libxgboost.so
Backward compatiblity
- The binary buffer file is not backward compatible with previous version.
- The model file is backward compatible on 64 bit platforms.
The model file is compatible between 64/32 bit platforms(not yet tested).
External memory version and other advanced features will be exposed to R library as well on linux.
- Previously some of the features are blocked due to C++11 and threading limits.
- The windows version is still blocked due to Rtools do not support std::thread.
rabit and dmlc-core are maintained through git submodule
- Anyone can open PR to update these dependencies now.

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.

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

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