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xgboost v0.80 Release Notes

Release Date: 2018-08-13 // over 5 years ago
    • ⬆️ JVM packages received a major upgrade : To consolidate the APIs and improve the user experience, we refactored the design of XGBoost4J-Spark in a significant manner. (#3387)
      • Consolidated APIs: It is now much easier to integrate XGBoost models into a Spark ML pipeline. Users can control behaviors like output leaf prediction results by setting corresponding column names. Training is now more consistent with other Estimators in Spark MLLIB: there is now one single method fit() to train decision trees.
      • Better user experience: we refactored the parameters relevant modules in XGBoost4J-Spark to provide both camel-case (Spark ML style) and underscore (XGBoost style) parameters
      • A brand-new tutorial is available for XGBoost4J-Spark.
      • Latest API documentation is now hosted at https://xgboost.readthedocs.io/.
    • 📚 XGBoost documentation now keeps track of multiple versions:
    • 👌 Support for per-group weights in ranking objective (#3379)
    • 🛠 Fix inaccurate decimal parsing (#3546)
    • 🆕 New functionality
      • Query ID column support in LIBSVM data files (#2749). This is convenient for performing ranking task in distributed setting.
      • Hinge loss for binary classification (binary:hinge) (#3477)
      • Ability to specify delimiter and instance weight column for CSV files (#3546)
      • Ability to use 1-based indexing instead of 0-based (#3546)
    • 👍 GPU support
      • Quantile sketch, binning, and index compression are now performed on GPU, eliminating PCIe transfer for 'gpu_hist' algorithm (#3319, #3393)
      • Upgrade to NCCL2 for multi-GPU training (#3404).
      • Use shared memory atomics for faster training (#3384).
      • Dynamically allocate GPU memory, to prevent large allocations for deep trees (#3519)
      • Fix memory copy bug for large files (#3472)
    • 📦 Python package
      • Importing data from Python datatable (#3272)
      • Pre-built binary wheels available for 64-bit Linux and Windows (#3424, #3443)
      • Add new importance measures 'total_gain', 'total_cover' (#3498)
      • Sklearn API now supports saving and loading models (#3192)
      • Arbitrary cross validation fold indices (#3353)
      • predict() function in Sklearn API uses best_ntree_limit if available, to make early stopping easier to use (#3445)
      • Informational messages are now directed to Python's print() rather than standard output (#3438). This way, messages appear inside Jupyter notebooks.
    • 📦 R package
      • Oracle Solaris support, per CRAN policy (#3372)
    • 📦 JVM packages
      • Single-instance prediction (#3464)
      • Pre-built JARs are now available from Maven Central (#3401)
      • Add NULL pointer check (#3021)
      • Consider spark.task.cpus when controlling parallelism (#3530)
      • Handle missing values in prediction (#3529)
      • Eliminate outputs of System.out (#3572)
    • 🔨 Refactored C++ DMatrix class for simplicity and de-duplication (#3301)
    • 🔨 Refactored C++ histogram facilities (#3564)
    • 🔨 Refactored constraints / regularization mechanism for split finding (#3335, #3429). Users may specify an elastic net (L2 + L1 regularization) on leaf weights as well as monotonic constraints on test nodes. The refactor will be useful for a future addition of feature interaction constraints.
    • Statically link libstdc++ for MinGW32 (#3430)
    • 👀 Enable loading from group, base_margin and weight (see here) for Python, R, and JVM packages (#3431)
    • Fix model saving for count:possion so that max_delta_step doesn't get truncated (#3515)
    • 🛠 Fix loading of sparse CSC matrix (#3553)
    • 🛠 Fix incorrect handling of base_score parameter for Tweedie regression (#3295)