--- title: "auto-sklearn vs xgboost" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/automl-auto-sklearn-vs-dmlc-xgboost" tools: ["automl-auto-sklearn", "dmlc-xgboost"] --- # auto-sklearn vs xgboost *GraphCanon updated Jul 11, 2026* ## Verdict Pick auto-sklearn when auto-sklearn is primarily Python; xgboost is C++; pick xgboost when xgboost is primarily C++; auto-sklearn is Python. [auto-sklearn](https://automl.github.io/auto-sklearn) reports 8.1k GitHub stars, 1.3k forks, and 210 open issues, last pushed Jun 29, 2026. [xgboost](https://xgboost.readthedocs.io/) has 29k stars, 8.9k forks, and 472 open issues, last pushed Jul 10, 2026. Figures are from public GitHub metadata via [auto-sklearn's repository](https://github.com/automl/auto-sklearn) and [xgboost's repository](https://github.com/dmlc/xgboost). | | [auto-sklearn](/tools/automl-auto-sklearn.md) | [xgboost](/tools/dmlc-xgboost.md) | | --- | --- | --- | | Tagline | Automated Machine Learning with scikit-learn | 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 | 8,119 | 28,553 | | Forks | 1,326 | 8,881 | | Open issues | 210 | 472 | | Language | Python | C++ | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | BSD-3-Clause | Apache-2.0 | | Categories | Computer Vision, Developer Tools, Model Training | Computer Vision | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [auto-sklearn](/tools/automl-auto-sklearn.md) | [xgboost](/tools/dmlc-xgboost.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 12d | 1d | | Open issues (now) | 210 | 472 | | Security scan | 22 low (22 low) | No lockfile | | Full report | [trust report](/tools/automl-auto-sklearn/trust.md) | [trust report](/tools/dmlc-xgboost/trust.md) | ## Choose when ### 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. ### 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: c++, distributed systems, gbdt, gbm. ## 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. ## 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](/tools/automl-auto-sklearn/alternatives) and [xgboost alternatives](/tools/dmlc-xgboost/alternatives) ([auto-sklearn markdown twin](/tools/automl-auto-sklearn/alternatives.md), [xgboost markdown twin](/tools/dmlc-xgboost/alternatives.md)), 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](/compare/automl-auto-sklearn-vs-dmlc-xgboost.md) 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](/tools/automl-auto-sklearn/trust); [xgboost trust report](/tools/dmlc-xgboost/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=automl-auto-sklearn`](/api/graphcanon/graph?tool=automl-auto-sklearn) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_