XGBoost (Extreme Gradient Boosting) is an open-source machine learning library designed to deliver high-performance implementations of gradient boosted decision trees. It is widely used for classification, regression, and ranking tasks, offering scalability, speed, and state-of-the-art accuracy. With support for CPU and GPU acceleration, distributed training, and integration across multiple programming languages such as Python, R, Java, and C++, XGBoost has become one of the most popular tools in both research and production environments.

Features of XGBoost:

Highly optimized, efficient implementation of gradient boosting algorithms.
Supports classification, regression, and ranking problems.
Provides regularization techniques to prevent overfitting.
Scales efficiently to large datasets with parallelized training.
Offers CPU and GPU acceleration for faster computation.
Integrates seamlessly with popular ML frameworks like Scikit-learn and Optuna.
Supports distributed training for large-scale machine learning tasks.
Actively maintained open-source project under the Apache 2.0 License.

To check the installed version of XGBoost, run these commands in your environment:

$ sudo su
$ sudo apt update
$ cd /opt/xgboost
$ source ~/xgb-venv/bin/activate
$ python -c "import xgboost as xgb; print('xgboost', xgb.__version__)"

Disclaimer: XGBoost is released under the Apache 2.0 License and maintained by the XGBoost community. Users are responsible for ensuring correct usage in their specific applications. The developers do not take responsibility for any consequences arising from its use. Always refer to the official documentation for the most accurate and up-to-date information.

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XGBoost

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XGBoost

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XGBoost
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