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and other bindings) that solves classification and regression on tabular data with efficient parallel tree boosting, scaling to billions of examples.","\u003Ch2 id=\"project-overview\">Project Overview\u003C\u002Fh2>\u003Cp>XGBoost (eXtreme Gradient Boosting) is an efficient, flexible, and portable distributed gradient boosting library implementing tree boosting algorithms such as GBDT. It is one of the most widely used machine learning libraries for tabular data competitions and industrial modeling. The same code runs on a single machine or distributed environments including Kubernetes, Hadoop, Spark, and Dask, handling training beyond billions of examples. Language bindings cover Python, R, Java, Scala, and C++.\u003C\u002Fp>\n\u003Cblockquote>\n\u003Cp>The evergreen of tabular modeling: one codebase from a laptop to a cluster.\u003C\u002Fp>\n\u003C\u002Fblockquote>\n\u003Ctable>\n\u003Cthead>\n\u003Ctr>\n\u003Cth>Item\u003C\u002Fth>\n\u003Cth>Value\u003C\u002Fth>\n\u003C\u002Ftr>\n\u003C\u002Fthead>\n\u003Ctbody>\u003Ctr>\n\u003Ctd>Platform\u003C\u002Ftd>\n\u003Ctd>Linux, macOS, Windows\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>Language\u003C\u002Ftd>\n\u003Ctd>C++ core, Python\u002FR\u002FJava\u002FScala\u002FC++ bindings\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>License\u003C\u002Ftd>\n\u003Ctd>Apache-2.0\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>Environments\u003C\u002Ftd>\n\u003Ctd>Single machine, Hadoop, Spark, Dask, Kubernetes\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003C\u002Ftbody>\u003C\u002Ftable>\n\u003Ch2 id=\"key-features\">Key Features\u003C\u002Fh2>\u003Cul>\n\u003Cli>\u003Cstrong>Gradient boosting\u003C\u002Fstrong>: parallel tree boosting (GBDT\u002FGBM) covering classification, regression, and ranking\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Distributed training\u003C\u002Fstrong>: the same code runs on Spark, Dask, Hadoop, and other clusters\u003C\u002Fli>\n\u003Cli>\u003Cstrong>GPU acceleration\u003C\u002Fstrong>: markedly faster training and inference on NVIDIA GPUs\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Sparsity aware\u003C\u002Fstrong>: handles missing values and sparse features automatically\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Regularization\u003C\u002Fstrong>: built-in L1\u002FL2 regularization and pruning to curb overfitting\u003C\u002Fli>\n\u003Cli>\u003Cstrong>CV and tuning\u003C\u002Fstrong>: built-in cross-validation, integrated with tuning libraries like Optuna\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Multi-language\u003C\u002Fstrong>: Python, R, JVM, and C++ bindings share one core\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch2 id=\"target-audience\">Target Audience\u003C\u002Fh2>\u003Cul>\n\u003Cli>Data scientists modeling tabular data\u003C\u002Fli>\n\u003Cli>Machine learning competition participants\u003C\u002Fli>\n\u003Cli>Engineers training tree models on large datasets\u003C\u002Fli>\n\u003Cli>Teams deploying models in production\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch2 id=\"quick-start\">Quick Start\u003C\u002Fh2>\u003Col>\n\u003Cli>Install the Python package:\u003C\u002Fli>\n\u003C\u002Fol>\n\u003Cdiv class=\"code-block\">\u003Cpre>\u003Ccode class=\"hljs language-bash\">pip install xgboost\u003C\u002Fcode>\u003C\u002Fpre>\u003C\u002Fdiv>\u003Col start=\"2\">\n\u003Cli>Train with the scikit-learn style interface:\u003C\u002Fli>\n\u003C\u002Fol>\n\u003Cdiv class=\"code-block\">\u003Cpre>\u003Ccode class=\"hljs language-python\">from xgboost import XGBClassifier\n\nmodel = XGBClassifier(n_estimators=300, max_depth=6)\nmodel.fit(X_train, y_train)\u003C\u002Fcode>\u003C\u002Fpre>\u003C\u002Fdiv>\u003Col start=\"3\">\n\u003Cli>Switch to the Dask or Spark interface for very large datasets; cluster code stays almost identical\u003C\u002Fli>\n\u003C\u002Fol>\n\u003Ch2 id=\"core-advantages\">Core Advantages\u003C\u002Fh2>\u003Cul>\n\u003Cli>\u003Cstrong>High accuracy\u003C\u002Fstrong>: tree boosting remains one of the strongest baselines on tabular tasks\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Scalability\u003C\u002Fstrong>: the same API from a laptop to thousands of nodes\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Mature engineering\u003C\u002Fstrong>: a decade of polish with complete docs, tutorials, and production cases\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch2 id=\"system-requirements\">System Requirements\u003C\u002Fh2>\u003Ctable>\n\u003Cthead>\n\u003Ctr>\n\u003Cth>Item\u003C\u002Fth>\n\u003Cth>Requirement\u003C\u002Fth>\n\u003C\u002Ftr>\n\u003C\u002Fthead>\n\u003Ctbody>\u003Ctr>\n\u003Ctd>OS\u003C\u002Ftd>\n\u003Ctd>Linux, macOS, Windows\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>Hardware\u003C\u002Ftd>\n\u003Ctd>CPU primary, optional GPU acceleration\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>Runtime\u003C\u002Ftd>\n\u003Ctd>Python 3.x \u002F R \u002F JVM\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003C\u002Ftbody>\u003C\u002Ftable>\n\u003Ch2 id=\"notes\">Notes\u003C\u002Fh2>\u003Cul>\n\u003Cli>\u003Cstrong>Data types\u003C\u002Fstrong>: convert input to DMatrix or numeric matrices; encode categorical features first\u003C\u002Fli>\n\u003Cli>\u003Cstrong>GPU setup\u003C\u002Fstrong>: the GPU build requires a matching CUDA environment\u003C\u002Fli>\n\u003C\u002Ful>\n",1793197846592]