XGBoost (Extreme Gradient Boosting) is an optimized machine learning algorithm designed for high performance and efficiency. It is widely used for structured data classification and regression tasks, offering superior speed and accuracy compared to traditional gradient boosting methods.

Features of XGBoost:

• Optimized gradient boosting for fast and scalable machine learning.
• Supports regression, classification, ranking, and user-defined prediction tasks.
• Regularization (L1 & L2) to reduce overfitting and improve model generalization.
• Distributed computing support for large-scale datasets.
• Parallel and GPU-accelerated training for faster performance.
• Integrated with popular machine learning frameworks like Scikit-Learn, TensorFlow, and PyTorch.
• Handles missing values and sparse data efficiently.
• Open-source, actively maintained, and widely used in industry and academia.

To check the installed XGBoost version, run:

python -c "import xgboost; print(xgboost.__version__)"

Disclaimer: XGBoost is an open-source software distributed under the Apache License 2.0. It is not affiliated with, endorsed by, or sponsored by any company. XGBoost is provided "as is," without any warranty, express or implied. Users assume full responsibility for its use. The developers and contributors to XGBoost hold no liability for any damages, losses, or consequences resulting from using this software. Users should comply with licensing terms and applicable regulations when deploying XGBoost.