Comparison
auto-sklearn vs xgboost
Verdict
Pick auto-sklearn when auto-sklearn is primarily Python; xgboost is C++; pick xgboost when xgboost is primarily C++; auto-sklearn is Python.
Markdown twin · auto-sklearn alternatives · xgboost alternatives
GraphCanon updated today
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Trust & integrity
| Signal | auto-sklearn | xgboost |
|---|---|---|
| Maintenance | Active (12d since push) As of today · github_public_v1 | Very active (1d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Organization account As of today · github_public_v1 | Not a fork · Organization account As of today · github_public_v1 |
| Security (OSV) | 22 low (22 low) As of today · osv@v1 | No lockfile As of today · none |
Tagline
- auto-sklearn
- Automated Machine Learning with scikit-learn
- xgboost
- 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 DataFlow
Stars
- auto-sklearn
- 8.1k
- xgboost
- 29k
Forks
- auto-sklearn
- 1.3k
- xgboost
- 8.9k
Open issues
- auto-sklearn
- 210
- xgboost
- 472
Language
- auto-sklearn
- Python
- xgboost
- C++
Adopt for
- auto-sklearn
- -
- xgboost
- -
Persona
- auto-sklearn
- -
- xgboost
- -
Runtime
- auto-sklearn
- -
- xgboost
- -
License
- auto-sklearn
- BSD-3-Clause
- xgboost
- Apache-2.0
Last pushed
- auto-sklearn
- Jun 29, 2026
- xgboost
- Jul 10, 2026
Categories
- auto-sklearn
- Computer Vision, Developer Tools, Model Training
- xgboost
- Computer Vision
Trust and health
Maintenance
- auto-sklearn
- Active (82%)
- xgboost
- Very active (96%)
Days since push
- auto-sklearn
- 12d
- xgboost
- 1d
Open issues (now)
- auto-sklearn
- 210
- xgboost
- 472
Security scan
- auto-sklearn
- 22 low (22 low)
- xgboost
- No lockfile
Full report
- auto-sklearn
- Trust report
- xgboost
- Trust report
Choose auto-sklearn if…
- auto-sklearn is primarily Python; xgboost is C++.
- License: auto-sklearn is BSD-3-Clause, xgboost is Apache-2.0.
- Tags unique to auto-sklearn: automated-machine-learning, automl, bayesian-optimization, hyperparameter-optimization.
- Also covers Developer Tools, Model Training.
- auto-sklearn ships Docker support for self-hosted deployment.
When NOT to use auto-sklearn
- Developer Tools: A gateway is overkill when you're pinned to a single provider and model.
- Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (automl/auto-sklearn) · observed Jul 11, 2026
- GitHub forks (automl/auto-sklearn) · observed Jul 11, 2026
- Last push (automl/auto-sklearn) · observed Jun 29, 2026
- License file (BSD-3-Clause) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (dmlc/xgboost) · observed Jul 11, 2026
- GitHub forks (dmlc/xgboost) · observed Jul 11, 2026
- Last push (dmlc/xgboost) · observed Jul 10, 2026
- License file (Apache-2.0) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: auto-sklearn 8.1k · xgboost 29k (synced Jul 11, 2026).
Common questions
- What is the difference between auto-sklearn and xgboost?
- auto-sklearn: Automated Machine Learning with scikit-learn. xgboost: 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 DataFlow. See the comparison table for live GitHub stats and shared categories.
- When should I choose auto-sklearn over xgboost?
- Choose auto-sklearn over xgboost when auto-sklearn is primarily Python; xgboost is C++; License: auto-sklearn is BSD-3-Clause, xgboost is Apache-2.0; Tags unique to auto-sklearn: automated-machine-learning, automl, bayesian-optimization, hyperparameter-optimization; Also covers Developer Tools, Model Training; auto-sklearn ships Docker support for self-hosted deployment.
- When should I choose xgboost over auto-sklearn?
- Choose xgboost over auto-sklearn when xgboost is primarily C++; auto-sklearn is Python; License: xgboost is Apache-2.0, auto-sklearn is BSD-3-Clause; Tags unique to xgboost: c++, distributed systems, gbdt, gbm.
- When should I avoid auto-sklearn?
- Developer Tools: A gateway is overkill when you're pinned to a single provider and model. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
- Is auto-sklearn or xgboost more popular on GitHub?
- xgboost has more GitHub stars (28,553 vs 8,119). Stars measure visibility, not whether either tool fits your constraints.
- Are auto-sklearn and xgboost open source?
- Yes - both are open-source projects on GitHub (auto-sklearn: BSD-3-Clause, xgboost: Apache-2.0).
- Where can I find alternatives to auto-sklearn or xgboost?
- GraphCanon lists graph-backed alternatives at auto-sklearn alternatives and xgboost alternatives (auto-sklearn markdown twin, xgboost markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, auto-sklearn or xgboost?
- auto-sklearn: Active. xgboost: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for auto-sklearn and xgboost?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: auto-sklearn trust report; xgboost trust report.