XGBoost
XGBoost (Extreme Gradient Boosting) is one of the most popular and powerful machine learning libraries for structured data. It is an optimized gradient boosting framework designed for high performance and speed. Unlike traditional decision tree algorithms, XGBoost incorporates regularization techniques to prevent overfitting, making it highly effective for predictive modeling.
XGBoost is widely used in Kaggle competitions and real-world business applications due to its scalability and accuracy. It supports parallel processing, making it significantly faster than other boosting algorithms. The library provides built-in handling of missing values, cross-validation, and hyperparameter tuning, making it a preferred choice for data scientists working on classification and regression problems.
The framework is used in industries such as finance (credit scoring and fraud detection), healthcare (disease prediction), and marketing (customer segmentation and churn analysis). Its ability to handle large datasets efficiently makes it a go-to tool for structured data analysis.