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xgboost · PyPI
https://pypi.org/project/xgboost/
WEBDec 19, 2023 · From PyPI. For a stable version, install using pip: pip install xgboost. For building from source, see build. Project details. Download files.
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Installation Guide — xgboost 2.0.3 documentation - Read the Docs
https://xgboost.readthedocs.io/en/stable/install.html
WEBSupported platforms are Linux (x86_64, aarch64), Windows (x86_64) and MacOS (x86_64, Apple Silicon). # Pip 21.3+ is required. pip install xgboost. You might need to run the command with --user flag or use virtualenv if you run into permission errors. Note.
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XGBoost Documentation — xgboost 2.1.0-dev documentation
https://xgboost.readthedocs.io/en/latest/index.html
WEBXGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way.
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Python API Reference — xgboost 2.0.3 documentation
https://xgboost.readthedocs.io/en/stable/python/python_api.html
WEBThis page gives the Python API reference of xgboost, please also refer to Python Package Introduction for more information about the Python package. Global Configuration. Core Data Structure. Learning API. Scikit-Learn API. Plotting API. Callback API. Dask API. Dask extensions for distributed training. Optional dask configuration. PySpark API ...
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GitHub - dmlc/xgboost: Scalable, Portable and Distributed …
https://github.com/dmlc/xgboost
WEBXGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way.
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Learn XGBoost in Python: A Step-by-Step Tutorial | DataCamp
https://www.datacamp.com/tutorial/xgboost-in-python
WEBDiscover the power of XGBoost, one of the most popular machine learning frameworks among data scientists, with this step-by-step tutorial in Python. From installation to creating DMatrix and building a classifier, this tutorial covers all the key aspects.
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How to Develop Your First XGBoost Model in Python
https://machinelearningmastery.com/develop-first-xgboost-model-python-scikit-learn/
WEBHow to install XGBoost on your system ready for use with Python. How to prepare data and train your first XGBoost model on a standard machine learning dataset. How to make predictions and evaluate the performance of a trained XGBoost model using scikit-learn. Do you have any questions about XGBoost or about this post?
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Difference between xgboost and py-xgboost? - Stack Overflow
https://stackoverflow.com/questions/60769425/difference-between-xgboost-and-py-xgboost
WEBMar 20, 2020 · It appears that xgboost and py-xgboost in the conda-forge repo are the same package, and both can be imported using. import xgboost as xgb. However, there are CPU-only versions of those two packages, and …
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XGBoost - Wikipedia
https://en.wikipedia.org/wiki/XGBoost
WEBXGBoost (eXtreme Gradient Boosting) is an open-source software library which provides a regularizing gradient boosting framework for C++, Java, Python, R, Julia, Perl, and Scala. It works on Linux, Microsoft Windows, and macOS.
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xgboost 2.0.3 on PyPI - Libraries.io
https://libraries.io/pypi/xgboost
WEBXGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way.
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