xgboost 2.0.3

XGBoost Python Package

Stars: 25517, Watchers: 25517, Forks: 8657, Open Issues: 432

The dmlc/xgboost repo was created 10 years ago and the last code push was an hour ago.
The project is extremely popular with a mindblowing 25517 github stars!

How to Install xgboost

You can install xgboost using pip

pip install xgboost

or add it to a project with poetry

poetry add xgboost

Package Details

Author

License

Apache-2.0

Homepage

PyPi:

https://pypi.org/project/xgboost/

GitHub Repo:

https://github.com/dmlc/xgboost

Classifiers

Related Packages

Errors

A list of common xgboost errors.

Code Examples

Here are some xgboost code examples and snippets.

GitHub Issues

The xgboost package has 432 open issues on GitHub

• Support latest pandas Index type.
• dart tree weight is not considered in global feature score.
• pandas.Int64Index is deprecated
• Avoid regenerating the gradient index for approx.
• Remove omp_get_max_threads in tree updaters.
• Weight parameter not used while using xgboost aft
• MAPE metric gives NaN when estimating zero loss residuals on a value of zero
• xgboost.(dask.)XGBRegressor and dask_ml.model_selection.HyperbandSearchCV: AttributeError: '(Dask)XGBRegressor' object has no attribute 'partial_fit'
• Number of threads used by predict.xgb.Booster (in R) cannot be specified and depends on previous runs
• Question on the second-order gradient of hinge-loss.
• [FEA] Make XGBoost4j-Spark to support PySpark
• 1.5.2 Patch Release.
• [Roadmap] Phasing out the support for old binary format.
• [XGBoost4J-Spark] Random Forest mode
• Training gets stuck and dumping model crashes

See more issues on GitHub