Comparison
auto-sklearn vs xgboost
Verdict
Pick auto-sklearn if auto-sklearn is an automated machine learning toolkit designed to automate hyperparameter optimization and function seamlessly with scikit-learn workflows; pick xgboost if xgboost: Scalable, portable gradient boosting library in C++ under Apache-2 license.
Markdown twin · auto-sklearn alternatives · xgboost alternatives
GraphCanon updated Sep 30, 2026
15views this month
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Trust & integrity
| Signal | auto-sklearn | xgboost |
|---|---|---|
| Maintenance | Active (14d since push) As of Sep 30, 2026 · github_public_v1 | Very active (0d since push) As of Sep 27, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 30, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 27, 2026 · github_public_v1 |
| OSV dependency advisories | Published findings As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- auto-sklearn
- Automated Machine Learning with scikit-learn
- xgboost
- Scalable, Portable and Distributed Gradient Boosting Library
Stars
- auto-sklearn
- 8.1k
- xgboost
- 29k
Forks
- auto-sklearn
- 1.3k
- xgboost
- 8.9k
Open issues
- auto-sklearn
- 209
- xgboost
- 442
Language
- auto-sklearn
- Python
- xgboost
- C++
Adopt for
- auto-sklearn
- auto-sklearn is an automated machine learning toolkit designed to automate hyperparameter optimization and function seamlessly with scikit-learn workflows.
- xgboost
- xgboost: Scalable, portable gradient boosting library in C++ under Apache-2 license
Persona
- auto-sklearn
- -
- xgboost
- -
Runtime
- auto-sklearn
- -
- xgboost
- -
License
- auto-sklearn
- BSD-3-Clause
- xgboost
- Apache-2.0 license allows free use, modification and distribution but requires preservation of copyright notices from source files and reproduction of license grants into any copyings of the codebase
Last pushed
- auto-sklearn
- Sep 15, 2026
- xgboost
- Sep 26, 2026
Categories
- auto-sklearn
- Model Training
- xgboost
- Model Training
Trust and health
Maintenance
- auto-sklearn
- Active (82%)
- xgboost
- Very active (96%)
Days since push
- auto-sklearn
- 14d
- xgboost
- 0d
Open issues (now)
- auto-sklearn
- 209
- xgboost
- 442
Stars delta
- auto-sklearn
- +7 (30d)
- xgboost
- +176 (30d)
Open issues delta
- auto-sklearn
- 0 (30d)
- xgboost
- +26 (30d)
OSV dependency advisories
- auto-sklearn
- Published findings
- xgboost
- No lockfile (source not queried)
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.
- auto-sklearn ships Docker support for self-hosted deployment.
- When you need a drop-in replacement estimator for your existing scikit-learn pipeline that can handle the complexity of hyperparameter tuning automatically.
When NOT to use auto-sklearn
- If extensive customization or control over individual machine learning components is required beyond what auto-sklearn's automation offers.
- In cases requiring non-scikit-learn model ensembles, as the toolkit primarily supports models that are part of the scikit-earn library.
Choose xgboost if…
- xgboost is primarily C++; auto-sklearn is Python.
- License: xgboost is Apache-2.0, auto-sklearn is BSD-3-Clause.
- Tags unique to xgboost: distributed-systems, gbdt, gbm, gbrt.
- Highly efficient for large datasets over billions of examples due to optimizations for speed and memory use.
When NOT to use xgboost
- Avoid if ease-of-use and quick model training are more important than fine-tuning or extreme scalability.
- Not suitable when the dataset fits comfortably in memory on a single node, where other simpler tools may exceed.
- Steer clear if your project does not require high-performance gradient boosting models for regression or classification.
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 Sep 30, 2026
- GitHub forks (automl/auto-sklearn) · observed Sep 30, 2026
- Last push (automl/auto-sklearn) · observed Sep 15, 2026
- License file (BSD-3-Clause) · observed Sep 30, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (dmlc/xgboost) · observed Sep 27, 2026
- GitHub forks (dmlc/xgboost) · observed Sep 27, 2026
- Last push (dmlc/xgboost) · observed Sep 26, 2026
- License file (Apache-2.0) · observed Sep 27, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: auto-sklearn 8.1k · xgboost 29k (synced Sep 30, 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 Library. 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; auto-sklearn ships Docker support for self-hosted deployment; When you need a drop-in replacement estimator for your existing scikit-learn pipeline that can handle the complexity of hyperparameter tuning automatically.
- 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: distributed-systems, gbdt, gbm, gbrt; Highly efficient for large datasets over billions of examples due to optimizations for speed and memory use.
- When should I avoid auto-sklearn?
- If extensive customization or control over individual machine learning components is required beyond what auto-sklearn's automation offers. In cases requiring non-scikit-learn model ensembles, as the toolkit primarily supports models that are part of the scikit-earn library.
- When should I avoid xgboost?
- Avoid if ease-of-use and quick model training are more important than fine-tuning or extreme scalability. Not suitable when the dataset fits comfortably in memory on a single node, where other simpler tools may exceed. Steer clear if your project does not require high-performance gradient boosting models for regression or classification.
- Is auto-sklearn or xgboost more popular on GitHub?
- xgboost has more GitHub stars (28,796 vs 8,134). 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.