1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
|
<img src="https://xgboost.ai/images/logo/xgboost-logo-trimmed.png" width=200/> eXtreme Gradient Boosting
===========
[](https://buildkite.com/xgboost/xgboost-ci)
[](https://github.com/dmlc/xgboost/actions)
[](https://xgboost.readthedocs.org)
[](./LICENSE)
[](https://cran.r-project.org/web/packages/xgboost)
[](https://pypi.python.org/pypi/xgboost/)
[](https://anaconda.org/conda-forge/py-xgboost)
[](https://optuna.org)
[](https://twitter.com/XGBoostProject)
[](https://api.securityscorecards.dev/projects/github.com/dmlc/xgboost)
[](https://colab.research.google.com/github/comet-ml/comet-examples/blob/master/integrations/model-training/xgboost/notebooks/how_to_use_comet_with_xgboost_tutorial.ipynb)
[Community](https://xgboost.ai/community) |
[Documentation](https://xgboost.readthedocs.org) |
[Resources](demo/README.md) |
[Contributors](CONTRIBUTORS.md) |
[Release Notes](https://xgboost.readthedocs.io/en/latest/changes/index.html)
XGBoost is an optimized distributed gradient boosting library designed to be highly ***efficient***, ***flexible*** and ***portable***.
It implements machine learning algorithms under the [Gradient Boosting](https://en.wikipedia.org/wiki/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.
The same code runs on major distributed environment (Kubernetes, Hadoop, SGE, Dask, Spark, PySpark) and can solve problems beyond billions of examples.
License
-------
© Contributors, 2021. Licensed under an [Apache-2](https://github.com/dmlc/xgboost/blob/master/LICENSE) license.
Contribute to XGBoost
---------------------
XGBoost has been developed and used by a group of active community members. Your help is very valuable to make the package better for everyone.
Checkout the [Community Page](https://xgboost.ai/community).
Reference
---------
- Tianqi Chen and Carlos Guestrin. [XGBoost: A Scalable Tree Boosting System](https://arxiv.org/abs/1603.02754). In 22nd SIGKDD Conference on Knowledge Discovery and Data Mining, 2016
- XGBoost originates from research project at University of Washington.
Sponsors
--------
Become a sponsor and get a logo here. See details at [Sponsoring the XGBoost Project](https://xgboost.ai/sponsors). The funds are used to defray the cost of continuous integration and testing infrastructure (https://xgboost-ci.net).
## Open Source Collective sponsors
[](#backers) [](#sponsors)
### Sponsors
[[Become a sponsor](https://opencollective.com/xgboost#sponsor)]
<a href="https://www.nvidia.com/en-us/" target="_blank"><img src="https://raw.githubusercontent.com/xgboost-ai/xgboost-ai.github.io/master/images/sponsors/nvidia.jpg" alt="NVIDIA" width="72" height="72"></a>
<a href="https://www.comet.com/site/?utm_source=xgboost&utm_medium=github&utm_content=readme" target="_blank"><img src="https://cdn.comet.ml/img/notebook_logo.png" height="72"></a>
<a href="https://opencollective.com/guest-f5ebfc79" target="_blank"><img src="https://images.opencollective.com/guest-f5ebfc79/avatar/256.png" height="72"></a>
### Backers
[[Become a backer](https://opencollective.com/xgboost#backer)]
<a href="https://opencollective.com/xgboost#backers" target="_blank"><img src="https://opencollective.com/xgboost/backers.svg?width=890"></a>
|
