XGBoost Explained: How Gradient Boosting Makes It a Top ML Algorithm

XGBoost, short for Extreme Gradient Boosting, is one of the most widely used machine learning algorithms for handling structured and tabular data. It is commonly applied to classification, regression, and ranking problems. Unlike a single Decision Tree or Random Forest, XGBoost builds multiple smaller decision trees sequentially, where each new tree learns from the errors of the previous one. This iterative error-correction process, known as gradient boosting, allows the model to progressively improve its predictions. The combined output of all trees makes XGBoost both highly accurate and efficient for a broad range of machine learning tasks.