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XGBoost Change Log

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

in progress version

  • Refactored gbm to allow more friendly cache strategy
    • Specialized some prediction routine
  • Automatically remove nan from input data when it is sparse.
    • This can solve some of user reported problem of istart != hist.size
  • Minor fixes
    • Thread local variable is upgraded so it is automatically freed at thread exit.
  • Migrate to C++11
    • The current master version now requires C++11 enabled compiled(g++4.8 or higher)
  • R package:
    • New parameters:
      • silent in xgb.DMatrix()
      • use_int_id in xgb.model.dt.tree()
    • Default value of the save_period parameter in xgboost() changed to NULL (consistent with xgb.train()).

v0.6 (2016.07.29)

  • Version 0.5 is skipped due to major improvements in the core
  • 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.
  • Add official option to approximate algorithm tree_method to parameter.
    • Change default behavior to switch to prefer faster algorithm.
    • User will get a message when approximate algorithm is chosen.
  • 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.
  • Improvements
    • Rabit and xgboost libs are not thread-safe and use thread local PRNGs
    • This could fix some of the previous problem which runs xgboost on multiple threads.
  • JVM Package
    • Enable xgboost4j for java and scala
    • XGBoost distributed now runs on Flink and Spark.
  • Support model attributes listing for meta data.
  • Support callback API
  • Support new booster DART(dropout in tree boosting)
  • Add CMake build system

v0.47 (2016.01.14)

  • 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.

v0.4 (2015.05.11)

  • 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

v0.3 (2014.09.07)

  • 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

v0.2x (2014.05.20)

  • Python module
  • Weighted samples instances
  • Initial version of pairwise rank

v0.1 (2014.03.26)

  • Initial release