XGBoost Gradient Boosting

XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible, and portable, implementing machine learning algorithms under the Gradient Boosting framework also known as GBDT, GBM, or GBRT. Originating from a research project at the University of Washington by Tianqi Chen and Carlos Guestrin, the seminal paper was presented at the 22nd SIGKDD Conference in 2016 and has since become one of the most cited machine learning papers with over 40,000 citations. The GitHub repository currently has 28,500 stars, 8,900 forks, 652 contributors, and 76 releases with version 3.3.0 as the latest stable release. The library is written in C++ at 44.7 percent of the codebase, with Python at 20.1 percent, CUDA at 18.1 percent, R at 7.2 percent, Scala at 4.6 percent, and Java at 3.3 percent. The same codebase runs on major distributed environments including Kubernetes, Hadoop, SGE, Dask, Spark, PySpark, Flink, and Google DataFlow, and can solve problems beyond billions of examples. GPU acceleration is provided through CUDA with NCCL for multi GPU communication, and the project is sponsored by NVIDIA and Intel. Key algorithmic innovations include sparsity aware split finding, approximate tree learning with weighted quantile sketch, parallelization at the column block level, and cache aware access patterns. The Apache 2.0 licensed project integrates with scikit-learn, Optuna for hyperparameter optimization, and supports objectives including regression, classification, ranking, and survival analysis.