XGBoost is a scalable tree boosting system developed by Tianqi Chen and Carlos Guestrin. It is widely used by data scientists to achieve state-of-the-art results in various machine learning tasks. The system incorporates a novel sparsity-aware algorithm for handling sparse data and a weighted quantile sketch for approximate tree learning. Key innovations include insights into cache access patterns, data compression, and sharding, enabling efficient processing of large datasets. XGBoost scales efficiently, often running faster than existing systems and handling billions of examples with minimal resources. The system is open-source and has been widely adopted in machine learning competitions, including Kaggle, where it was used in over half of the winning solutions. XGBoost's success is attributed to its scalability, efficiency, and ability to handle both dense and sparse data. It supports various learning objectives, including regularized learning, and incorporates techniques like shrinkage and column subsampling to prevent overfitting. XGBoost's system design includes a column block structure for parallel learning, which helps in efficiently sorting and processing data. It also employs cache-aware prefetching and block sharding to optimize performance on both single-machine and distributed settings. The system supports out-of-core computation, allowing it to handle large datasets that do not fit into memory. XGBoost has been evaluated on various datasets, including the Allstate, Higgs, Yahoo! Learning to Rank, and Criteo datasets. It outperforms other systems in terms of speed and accuracy, particularly in distributed and out-of-core settings. The system's ability to scale efficiently and handle large-scale problems with limited resources makes it a valuable tool in machine learning.XGBoost is a scalable tree boosting system developed by Tianqi Chen and Carlos Guestrin. It is widely used by data scientists to achieve state-of-the-art results in various machine learning tasks. The system incorporates a novel sparsity-aware algorithm for handling sparse data and a weighted quantile sketch for approximate tree learning. Key innovations include insights into cache access patterns, data compression, and sharding, enabling efficient processing of large datasets. XGBoost scales efficiently, often running faster than existing systems and handling billions of examples with minimal resources. The system is open-source and has been widely adopted in machine learning competitions, including Kaggle, where it was used in over half of the winning solutions. XGBoost's success is attributed to its scalability, efficiency, and ability to handle both dense and sparse data. It supports various learning objectives, including regularized learning, and incorporates techniques like shrinkage and column subsampling to prevent overfitting. XGBoost's system design includes a column block structure for parallel learning, which helps in efficiently sorting and processing data. It also employs cache-aware prefetching and block sharding to optimize performance on both single-machine and distributed settings. The system supports out-of-core computation, allowing it to handle large datasets that do not fit into memory. XGBoost has been evaluated on various datasets, including the Allstate, Higgs, Yahoo! Learning to Rank, and Criteo datasets. It outperforms other systems in terms of speed and accuracy, particularly in distributed and out-of-core settings. The system's ability to scale efficiently and handle large-scale problems with limited resources makes it a valuable tool in machine learning.