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XGBoost is an optimized and scalable machine learning library designed for efficiency, speed, and flexibility. It is widely used for tasks such as regression, classification, and ranking due to its ability to handle large datasets and improve model performance.

Features of XGBoost:

XGBoost offers high performance and speed by optimizing the use of system resources, including both CPU and GPU acceleration, for faster model training.
It supports regularization techniques (L1 and L2) to reduce overfitting, making it suitable for complex data models.
XGBoost handles missing data internally, making it more robust and effective for real-world datasets.
It supports various objective functions, including regression, classification, and ranking, with extensive customization options.
XGBoost allows parallel computation, tree pruning, and out-of-core computing for handling large datasets more efficiently.

To check the version, run
$ python3
import xgboost as xgb
print(xgb.__version__)

Disclaimer: XGBoost is distributed under the Apache 2.0 license and is provided free of charge. It is not affiliated with, endorsed by, or sponsored by any company. XGBoost is provided "as is," without any warranty, express or implied. Users utilize this software at their own risk. The developers and contributors to XGBoost hold no responsibility for any damages, losses, or consequences resulting from the use of this software. Users are advised to carefully review and comply with licensing terms and any applicable regulations while using XGBoost.

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Xgboost

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Xgboost
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