#1313th of 15 in ML frameworks and libraries
xgboost
Gradient boosted decision tree library for tabular machine learning
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- Stars
- 28.9k
- License
- Apache-2.0
- Last commit
- Oct 2026
- Last release
- Sep 2026
- Language
- C++
28,013 on 24 Mar 202628,868 on 13 Oct 2026
From GitHub's daily star counts.
Checked on GitHub.
Overview
XGBoost implements parallel tree boosting (GBDT/GBM) under the gradient boosting framework. Per the README, the same code runs on Kubernetes, Hadoop, SGE, Dask, Spark and PySpark, and scales to billions of examples. It is distributed through PyPI, CRAN and conda-forge.
Who it is for: Data scientists and ML engineers training models on tabular data
Strengths
- Same code runs on Kubernetes, Hadoop, SGE, Dask, Spark and PySpark
- Packaged on PyPI, CRAN and conda-forge
- Backed by a KDD 2016 paper; Apache-2.0 licensed
- Active: release on 2026-09-15 and recent commits
Weaknesses
- Tree boosting only; not a general deep learning framework
- README does not state GPU support, RAM needs or language bindings
- README has no install or quick-start example; relies on external docs
- Aimed at tabular data; no LLM or generative model support
What it needs
- Models: gradient boosted decision trees
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