XGBoost (eXtreme Gradient Boosting)

XGBoost (eXtreme Gradient Boosting) is a library that implements the gradient boosting machine learning technique with high speed and high accuracy. It handles diverse prediction tasks—classification, regression, and ranking—and is extremely popular in data analysis competitions such as Kaggle as well as in real-world production environments. Key features of XGBoost: • An ensemble learning model based on decision trees with strong generalization performance • Built-in support for parallel computation, missing-value handling, regularization, and cross-validation • Compatible with both CPU and GPU, enabling fast processing even on large datasets • Multi-language support (Python, R, Java, C++, etc.) for flexible development XGBoost works by training multiple weak learners (typically decision trees) sequentially, with each model correcting the errors of the previous one—a method known as gradient boosting. As training progresses, the model is refined and predictive accuracy improves. Typical use cases: • Customer churn prediction and purchase tendency analysis • Medical diagnosis and financial scoring models • Fraud detection and anomaly detection • Precise prediction for numerical regression and ranking tasks Compared to deep learning, XGBoost excels in interpretability and training speed, and remains one of the most trusted methods for tabular/structured data. Its accuracy and flexibility make it widely used across both academic research and industry practice.