XGBoost
by Community
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and D
OSS
XGBoost
Added 1 June 2026
Overview
XGBoost is a gradient boosting library that trains decision tree ensembles for classification, regression, and ranking tasks. It runs on single machines or distributed systems like Spark, Hadoop, and Dask, with bindings for Python, R, Java, Scala, and C++.
Best for
Best for
Data scientists and ML engineers building production models on structured datasets.
Use cases
- Building high-accuracy predictive models for tabular data
- Training models at scale across distributed clusters
- Competing in machine learning competitions
Notes
XGBoost is a gradient boosting library that trains decision tree ensembles for classification, regression, and ranking tasks. It runs on single machines or distributed systems like Spark, Hadoop, and Dask, with bindings for Python, R, Java, Scala, and C++.
28,431 stars on GitHub. Last updated 2026-05-28. Licensed Apache-2.0.
Use cases
- Building high-accuracy predictive models for tabular data
- Training models at scale across distributed clusters
- Competing in machine learning competitions
Pros
- Consistently outperforms other gradient boosting implementations on structured data
- Handles both single-machine and distributed training without code changes
- Mature ecosystem with extensive documentation and community support
Cons
- Requires careful hyperparameter tuning to avoid overfitting
- Slower than simpler models for real-time inference on resource-constrained devices
- Works best on tabular data, not designed for images or text
Indexed from awesome-llmops and enriched against its public facts.
Pros
- Consistently outperforms other gradient boosting implementations on structured data
- Handles both single-machine and distributed training without code changes
- Mature ecosystem with extensive documentation and community support
Cons
- Requires careful hyperparameter tuning to avoid overfitting
- Slower than simpler models for real-time inference on resource-constrained devices
- Works best on tabular data, not designed for images or text
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