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Latest Version
Avg Release Cycle
89 days
Latest Release
1226 days ago

Changelog History
Page 3

  • v0.71 Changes

    April 11, 2018
    • 🚀 This is a minor release, mainly motivated by issues concerning pip install, e.g. #2426, #3189, #3118, and #3194. With this release, users of Linux and MacOS will be able to run pip install for the most part.
    • 🔨 Refactored linear booster class (gblinear), so as to support multiple coordinate descent updaters (#3103, #3134). See BREAKING CHANGES below.
    • 🛠 Fix slow training for multiclass classification with high number of classes (#3109)
    • 🛠 Fix a corner case in approximate quantile sketch (#3167). Applicable for 'hist' and 'gpu_hist' algorithms
    • 🛠 Fix memory leak in DMatrix (#3182)
    • 🆕 New functionality
      • Better linear booster class (#3103, #3134)
      • Pairwise SHAP interaction effects (#3043)
      • Cox loss (#3043)
      • AUC-PR metric for ranking task (#3172)
      • Monotonic constraints for 'hist' algorithm (#3085)
    • 👍 GPU support
      • Create an abtract 1D vector class that moves data seamlessly between the main and GPU memory (#2935, #3116, #3068). This eliminates unnecessary PCIe data transfer during training time.
      • Fix minor bugs (#3051, #3217)
      • Fix compatibility error for CUDA 9.1 (#3218)
    • 📦 Python package:
      • Correctly handle parameter verbose_eval=0 (#3115)
    • 📦 R package:
      • Eliminate segmentation fault on 32-bit Windows platform (#2994)
    • 📦 JVM packages
      • Fix a memory bug involving double-freeing Booster objects (#3005, #3011)
      • Handle empty partition in predict (#3014)
      • Update docs and unify terminology (#3024)
      • Delete cache files after job finishes (#3022)
      • Compatibility fixes for latest Spark versions (#3062, #3093)
    • 💥 BREAKING CHANGES: Updated linear modelling algorithms. In particular L1/L2 regularisation penalties are now normalised to number of training examples. This makes the implementation consistent with sklearn/glmnet. L2 regularisation has also been removed from the intercept. To produce linear models with the old regularisation behaviour, the alpha/lambda regularisation parameters can be manually scaled by dividing them by the number of training examples.

  • v0.60 Changes

    July 29, 2016

    🔄 Changes

    • 🔖 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 Changes

    January 15, 2016

    🚀 This is last version release of 0.4 series, with many changes in the language bindings.

    This is also a checkpoint before we switch to xgboost-brick #736

    🔄 Changes

    • 🔄 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.

  • v0.40 Changes

    May 12, 2015

    🚀 This is a stable release of 0.4 version

  • v0.7 Changes

    December 30, 2017

    🔄 Changes

    • 🚀 This version represents a major change from the last release (v0.6), which was released one year and half ago.
    • ⚡️ Updated Sklearn API
      • Add compatibility layer for scikit-learn v0.18: sklearn.cross_validation now deprecated
      • Updated to allow use of all XGBoost parameters via **kwargs.
      • Updated nthread to n_jobs and seed to random_state (as per Sklearn convention); nthread and seed are now marked as deprecated
      • Updated to allow choice of Booster (gbtree, gblinear, or dart)
      • XGBRegressor now supports instance weights (specify sample_weight parameter)
      • Pass n_jobs parameter to the DMatrix constructor
      • Add xgb_model parameter to fit method, to allow continuation of training
    • 🔨 Refactored gbm to allow more friendly cache strategy
      • Specialized some prediction routine
    • 📜 Robust DMatrix construction from a sparse matrix
    • Faster consturction of DMatrix from 2D NumPy matrices: elide copies, use of multiple threads
    • 🚚 Automatically remove nan from input data when it is sparse.
      • This can solve some of user reported problem of istart != hist.size
    • 🛠 Fix the single-instance prediction function to obtain correct predictions
    • 🛠 Minor fixes
      • Thread local variable is upgraded so it is automatically freed at thread exit.
      • Fix saving and loading count::poisson models
      • Fix CalcDCG to use base-2 logarithm
      • Messages are now written to stderr instead of stdout
      • Keep built-in evaluations while using customized evaluation functions
      • Use bst_float consistently to minimize type conversion
      • Copy the base margin when slicing DMatrix
      • Evaluation metrics are now saved to the model file
      • Use int32_t explicitly when serializing version
      • In distributed training, synchronize the number of features after loading a data matrix.
    • Migrate to C++11
      • The current master version now requires C++11 enabled compiled(g++4.8 or higher)
    • ⚡️ Predictor interface was factored out (in a manner similar to the updater interface).
    • 👉 Makefile support for Solaris and ARM
    • ✅ Test code coverage using Codecov
    • ➕ Add CPP tests
    • ➕ Add Dockerfile and Jenkinsfile to support continuous integration for GPU code
    • 🆕 New functionality
      • Ability to adjust tree model's statistics to a new dataset without changing tree structures.
      • Ability to extract feature contributions from individual predictions, as described in here and here.
      • Faster, histogram-based tree algorithm (tree_method='hist') .
      • GPU/CUDA accelerated tree algorithms (tree_method='gpu_hist' or 'gpu_exact'), including the GPU-based predictor.
      • Monotonic constraints: when other features are fixed, force the prediction to be monotonic increasing with respect to a certain specified feature.
      • Faster gradient caculation using AVX SIMD
      • Ability to export models in JSON format
      • Support for Tweedie regression
      • Additional dropout options for DART: binomial+1, epsilon
      • Ability to update an existing model in-place: this is useful for many applications, such as determining feature importance
    • 📦 Python package:
      • New parameters:
      • learning_rates in cv()
      • shuffle in mknfold()
      • max_features and show_values in plot_importance()
      • sample_weight in XGBRegressor.fit()
      • Support binary wheel builds
      • Fix MultiIndex detection to support Pandas 0.21.0 and higher
      • Support metrics and evaluation sets whose names contain -
      • Support feature maps when plotting trees
      • Compatibility fix for Python 2.6
      • Call print_evaluation callback at last iteration
      • Use appropriate integer types when calling native code, to prevent truncation and memory error
      • Fix shared library loading on Mac OS X
    • 📦 R package:
      • New parameters:
      • silent in xgb.DMatrix()
      • use_int_id in xgb.model.dt.tree()
      • predcontrib in predict()
      • monotone_constraints in xgb.train()
      • Default value of the save_period parameter in xgboost() changed to NULL (consistent with xgb.train()).
      • It's possible to custom-build the R package with GPU acceleration support.
      • Enable JVM build for Mac OS X and Windows
      • Integration with AppVeyor CI
      • Improved safety for garbage collection
      • Store numeric attributes with higher precision
      • Easier installation for devel version
      • Improved xgb.plot.tree()
      • Various minor fixes to improve user experience and robustness
      • Register native code to pass CRAN check
      • Updated CRAN submission
    • 📦 JVM packages
      • Add Spark pipeline persistence API
      • Fix data persistence: loss evaluation on test data had wrongly used caches for training data.
      • Clean external cache after training
      • Implement early stopping
      • Enable training of multiple models by distinguishing stage IDs
      • Better Spark integration: support RDD / dataframe / dataset, integrate with Spark ML package
      • XGBoost4j now supports ranking task
      • Support training with missing data
      • Refactor JVM package to separate regression and classification models to be consistent with other machine learning libraries
      • Support XGBoost4j compilation on Windows
      • Parameter tuning tool
      • Publish source code for XGBoost4j to maven local repo
      • Scala implementation of the Rabit tracker (drop-in replacement for the Java implementation)
      • Better exception handling for the Rabit tracker
      • Persist num_class, number of classes (for classification task)
      • XGBoostModel now holds BoosterParams
      • libxgboost4j is now part of CMake build
      • Release DMatrix when no longer needed, to conserve memory
      • Expose baseMargin, to allow initialization of boosting with predictions from an external model
      • Support instance weights
      • Use SparkParallelismTracker to prevent jobs from hanging forever
      • Expose train-time evaluation metrics via XGBoostModel.summary
      • Option to specify host-ip explicitly in the Rabit tracker
    • 📚 Documentation
      • Better math notation for gradient boosting
      • Updated build instructions for Mac OS X
      • Template for GitHub issues
      • Add CITATION file for citing XGBoost in scientific writing
      • Fix dropdown menu in xgboost.readthedocs.io
      • Document updater_seq parameter
      • Style fixes for Python documentation
      • Links to additional examples and tutorials
      • Clarify installation requirements
    • 🔄 Changes that break backward compatibility
      • #1519 XGBoost-spark no longer contains APIs for DMatrix; use the public booster interface instead.
      • #2476 XGBoostModel.predict() now has a different signature