Understanding Gradient Boosted Trees: The Foundation of XGBoost
Last Updated on December 2, 2025 by Editorial Team
Author(s): Utkarsh Mittal
Originally published on Towards AI.
Understanding Gradient Boosted Trees: The Foundation of XGBoost
Gradient Boosted Trees have revolutionized machine learning, powering some of the most successful algorithms in data science. Before diving into the complexities of XGBoost, it’s essential to understand how the gradient boosted tree algorithm works at its core. This article will walk you through the iterative process that makes this algorithm so powerful.
This article explains the iterative process of the gradient boosted tree algorithm, detailing how it achieves improvement through successive decision trees that predict errors from prior models. Key concepts such as the base tree, correction trees, and the importance of shrinkage to prevent overfitting are discussed, leading to a comprehensive understanding of how XGBoost excels in capturing complex data patterns and optimizing predictions effectively.
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