XGBoost is a popular machine learning algorithm that implements an extreme gradient boosting method to be applied to different supervised learning problems. Gradient boosting helps minimize the error in each successive step, improving prediction accuracy during the learning process for both regression and classification problems.
XGBoost offers a few technical advantages over other gradient boosting approaches, including a more direct route to the minimum error, converging more quickly with fewer steps, and simplified calculations to improve speed and lower compute costs. XGBoost is available in several common machine learning libraries, like scikit-learn for Python users and caret for R users.
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