xgboost
XGBoost Python Package
Decision gist · record as of 2026-08-14
Yes. XGBoost is a mature, actively maintained library with no known vulnerabilities, permissive licensing, and strong community adoption. Medium install friction is manageable given the availability of pre-built wheels for all major platforms. Install if you need gradient boosting for tabular data or distributed training; the scikit-learn-compatible API makes it straightforward to integrate into existing ML workflows.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.12 or later; GPU support requires nvidia-nccl-cu13 and compatible CUDA environment.
- Medium install friction due to compiled wheels for multiple platforms (x86_64, arm64, macOS, Windows, Linux).
- Requires numpy and scipy; nvidia-nccl-cu13 is a runtime dependency for GPU support.
License · maintenance · safety
Apache-2.0 (permissive) — Licensed under Apache-2.0 (permissive), allowing commercial use, modification, and distribution with minimal restrictions—suitable for proprietary projects.
last release 2026-08-04 (10 days) · last repo commit 2026-08-13 · 28,652 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 53,370,538 downloads/mo, #552 on PyPI
Alternatives
Verify before relying
pip install xgboost
import xgboost as xgb
model = xgb.XGBClassifier()
model.fit(X_train, y_train)
predictions = model.predict(X_test)- Exact performance gains or scalability limits for billion-example datasets mentioned in description.
- Whether nvidia-nccl-cu13 is optional or required for standard CPU-only installations.
What it is and what it does
XGBoost is a production-grade gradient boosting library that builds ensembles of decision trees to solve classification, regression, and ranking problems. It is designed for speed and accuracy, implementing parallel tree boosting (GBDT/GBM) with support for distributed execution across major platforms like Kubernetes, Hadoop, Spark, and Dask. The library depends on numpy and scipy for numerical operations, and optionally on nvidia-nccl-cu13 for GPU acceleration.
The package is widely used in competitive machine learning and production systems where model accuracy and training speed matter. It exposes a scikit-learn-compatible API (XGBClassifier, XGBRegressor) alongside a lower-level functional interface, making it accessible to both practitioners and researchers. The codebase is actively maintained, supports modern Python versions (3.12+), and carries no known security vulnerabilities.
Use it for
- Train gradient boosting models for Kaggle competitions or production classification/regression tasks.
- Distribute model training across a Spark or Dask cluster for datasets too large for a single machine.
- Accelerate training on GPU hardware using CUDA-enabled environments with nvidia-nccl-cu13.
- Benchmark tree-based ensemble methods against neural networks or other algorithms in research.
- Deploy pre-trained XGBoost models in production pipelines for real-time or batch predictions.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
XGBoost is a mature, actively maintained library with no known vulnerabilities, permissive licensing, and strong community adoption. Medium install friction is manageable given the availability of pre-built wheels for all major platforms. Install if you need gradient boosting for tabular data or distributed training; the scikit-learn-compatible API makes it straightforward to integrate into existing ML workflows.
Install
xgboost on PyPI
Before you install
Medium install friction due to compiled wheels for multiple platforms (x86_64, arm64, macOS, Windows, Linux). Requires numpy and scipy; nvidia-nccl-cu13 is a runtime dependency for GPU support. Package is actively maintained with a release 10 days old and strong community backing.
Requires Python 3.12 or later; GPU support requires nvidia-nccl-cu13 and compatible CUDA environment.
License in practice
Licensed under Apache-2.0 (permissive), allowing commercial use, modification, and distribution with minimal restrictions—suitable for proprietary projects.
Quickstart
pip install xgboost
import xgboost as xgb
model = xgb.XGBClassifier()
model.fit(X_train, y_train)
predictions = model.predict(X_test)Verify before relying
- Exact performance gains or scalability limits for billion-example datasets mentioned in description.
- Whether nvidia-nccl-cu13 is optional or required for standard CPU-only installations.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.12 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 3 packagesnumpyscipynvidia-nccl-cu13 |
| Maintenance | Actively maintained 10 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 53,370,538 / month, #552 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Typing :: Typed |
Evidence: xgboost-3.4.0-py3-none-macosx_10_15_x86_64.whl; xgboost-3.4.0-py3-none-macosx_12_0_arm64.whl; xgboost-3.4.0-py3-none-manylinux_2_28_aarch64.whl; xgboost-3.4.0-py3-none-manylinux_2_28_x86_64.whl; xgboost-3.4.0-py3-none-win_amd64.whl; xgboost-3.4.0-py3-none-win_arm64.whl
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