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XGBoost (Extreme Gradient Boosting) is implemented in machine learning algorithms under the Gradient Boosting framework. It provides high-performance implementation of gradient boosted decision trees. XGBoost is portable, flexible, and efficient. It provides highly optimised, scalable and fast implementations of gradient boosting. 

XGBoost is widely used for both classification and regression tasks and has proven to be highly effective in various machine learning competitions and real-world applications. While decision trees themselves can be used for these tasks, XGBoost enhances their performance by employing a boosting technique, which combines the predictions of multiple weak learners (trees) to create a strong learner. It often outperforms other models and is known for its speed, accuracy, and ability to handle complex relationships in data.

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