XGBoost, short for eXtreme Gradient Boosting, is a highly efficient and scalable machine learning library designed for supervised learning tasks such as classification and regression. It implements the gradient boosting framework, which builds an ensemble of decision trees in a sequential manner to improve predictive performance.
Key Features of XGBoost:
- High Performance: XGBoost is optimized for speed and efficiency, enabling rapid training and prediction times.
- Scalability: It supports distributed computing, allowing it to handle large datasets across multiple machines.
- Regularization: XGBoost includes built-in regularization techniques to prevent overfitting, enhancing the model’s generalization capabilities.
- Flexibility: The library supports various objective functions and evaluation metrics, making it adaptable to a wide range of machine learning problems.
Applications of XGBoost:
XGBoost has been widely adopted in various domains due to its robustness and performance. Notably, it has been the algorithm of choice for many winning solutions in machine learning competitions.
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