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XGBoost (eXtreme Gradient Boosting) is an optimized and highly efficient implementation of the Gradient Boosting framework, widely used for supervised machine learning tasks such as classification, regression, and ranking. It is known for its speed, performance, and accuracy, making it a popular choice in machine learning competitions and real-world applications.
It builds upon traditional gradient boosting but introduces several improvements such as regularization techniques, handling missing values, and parallelized computations, which significantly enhance its performance. Due to its scalability, it works well on both small datasets and large-scale distributed environments.
XGBoost (eXtreme Gradient Boosting) is an optimized and highly efficient implementation of the Gradient Boosting framework, widely used for supervised machine learning tasks such as classification, regression, and ranking. It is known for its speed, performance, and accuracy, making it a popular choice in machine learning competitions and real-world applications.
It builds upon traditional gradient boosting but introduces several improvements such as regularization techniques, handling missing values, and parallelized computations, which significantly enhance its performance. Due to its scalability, it works well on both small datasets and large-scale distributed environments.