dmlc/xgboost

Health v2 · maintenance only

Adoption not part of the score

28694 stars · 8890 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

XGBoost is an optimized, scalable gradient boosting library implementing parallel tree boosting (GBDT/GBM) with bindings for Python, R, Java, Scala, C++, and more. It runs on a single machine or distributed environments such as Spark, Dask, Kubernetes, and Ray, with GPU support and the ability to handle billions of examples.

Use cases

• train gradient boosted tree models on tabular data
• win kaggle-style classification and regression problems
• train XGBoost models on a Spark or Dask cluster
• accelerate model training with NVIDIA GPUs
• build a ranking model with learning to rank
• handle categorical features and missing values in tree models
• serve fast predictions for structured data

When to choose

• you need state-of-the-art accuracy on structured/tabular data
• your dataset is too large for one machine and needs distributed training
• you want mature multi-language bindings and GPU acceleration
• you need battle-tested, widely supported ML tooling

When to avoid

• you are working with unstructured data like images, audio, or raw text where deep learning excels
• you need very low-latency, tiny models for embedded deployment
• you want simple interpretable linear models
• you need online/incremental learning as a primary workflow

Facets

library · maturity stable

machine-learning gpu-computing data-science machine-learning data-science microservices python jvm cpp cross-platform cloud gradient-boosting gbdt tree-ensembles tabular-data spark dask scikit-learn gpu

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