--- title: "RoBO vs hyperband" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/automl-robo-vs-zygmuntz-hyperband" tools: ["automl-robo", "zygmuntz-hyperband"] --- # RoBO vs hyperband *GraphCanon updated Aug 4, 2026* ## Verdict Pick RoBO if roBO is a Python framework for robust Bayesian optimization using Gaussian processes and random forests; pick hyperband if hyperband optimizes hyperparameters quickly with an efficient bandit-based approach, supporting several models from scikit-learn and polylearn. [RoBO](https://github.com/automl/RoBO) reports 492 GitHub stars, 129 forks, and 25 open issues, last pushed Apr 30, 2019. [hyperband](http://fastml.com/tuning-hyperparams-fast-with-hyperband/) has 599 stars, 73 forks, and 9 open issues, last pushed Aug 15, 2018. Figures are from public GitHub metadata via [RoBO's repository](https://github.com/automl/RoBO) and [hyperband's repository](https://github.com/zygmuntz/hyperband). | | [RoBO](/tools/automl-robo.md) | [hyperband](/tools/zygmuntz-hyperband.md) | | --- | --- | --- | | Tagline | A Robust Bayesian Optimization framework | Tuning hyperparams fast with Hyperband | | Stars | 492 | 599 | | Forks | 129 | 73 | | Open issues | 25 | 9 | | Language | Python | Python | | Adopt for | RoBO is a Python framework for robust Bayesian optimization using Gaussian processes and random forests. | Hyperband optimizes hyperparameters quickly with an efficient bandit-based approach, supporting several models from scikit-learn and polylearn. | | Persona | - | - | | Runtime | - | - | | License | BSD-3-Clause | Other | | Categories | Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [RoBO](/tools/automl-robo.md) | [hyperband](/tools/zygmuntz-hyperband.md) | | --- | --- | --- | | Days since push | 2653d | 2910d | | Open issues (now) | 25 | 9 | | Owner type | Organization | User | | Full report | [trust report](/tools/automl-robo/trust.md) | [trust report](/tools/zygmuntz-hyperband/trust.md) | ## Shared compatibility - **Python**: [RoBO](/tools/automl-robo.md) - Python runtime; [hyperband](/tools/zygmuntz-hyperband.md) - Python runtime ## Decision facts: RoBO - **Adopt for:** RoBO is a Python framework for robust Bayesian optimization using Gaussian processes and random forests. ## Decision facts: hyperband - **Adopt for:** Hyperband optimizes hyperparameters quickly with an efficient bandit-based approach, supporting several models from scikit-learn and polylearn. ## Choose when ### Choose RoBO if… - License: RoBO is BSD-3-Clause, hyperband is Other. - Tags unique to RoBO: bayesian-optimization, gaussian processes, python, random forests. - For tasks requiring robust handling of noisy data in Bayesian Optimization ### Choose hyperband if… - License: hyperband is Other, RoBO is BSD-3-Clause. - Tags unique to hyperband: classification, gradient-boosting, hyperparameter-optimization, machine-learning. - Use Hyperband when you need fast optimization of hyperparameters for classifiers such as gradient boosting or regressors like factorization machines from polylearn. ## When NOT to use RoBO - Avoid if your project strictly requires open-source licenses other than BSD-3-Clause - Not suitable for users not comfortable installing external dependencies manually ## When NOT to use hyperband - Avoid Hyperband if you require custom data formats that differ significantly from scikit-learn conventions, as this will necessitate extensive customization of the load_data modules. - Do not use Hyperband when the models you need for hyperparameter tuning are not among the eight pre-supported models; additional support is required outside what comes built-in. ## Common questions ### What is the difference between RoBO and hyperband? RoBO: A Robust Bayesian Optimization framework. hyperband: Tuning hyperparams fast with Hyperband. See the comparison table for live GitHub stats and shared categories. ### When should I choose RoBO over hyperband? Choose RoBO over hyperband when License: RoBO is BSD-3-Clause, hyperband is Other; Tags unique to RoBO: bayesian-optimization, gaussian processes, python, random forests; For tasks requiring robust handling of noisy data in Bayesian Optimization. ### When should I choose hyperband over RoBO? Choose hyperband over RoBO when License: hyperband is Other, RoBO is BSD-3-Clause; Tags unique to hyperband: classification, gradient-boosting, hyperparameter-optimization, machine-learning; Use Hyperband when you need fast optimization of hyperparameters for classifiers such as gradient boosting or regressors like factorization machines from polylearn. ### When should I avoid RoBO? Avoid if your project strictly requires open-source licenses other than BSD-3-Clause Not suitable for users not comfortable installing external dependencies manually ### When should I avoid hyperband? Avoid Hyperband if you require custom data formats that differ significantly from scikit-learn conventions, as this will necessitate extensive customization of the load_data modules. Do not use Hyperband when the models you need for hyperparameter tuning are not among the eight pre-supported models; additional support is required outside what comes built-in. ### Is RoBO or hyperband more popular on GitHub? hyperband has more GitHub stars (599 vs 492). Stars measure visibility, not whether either tool fits your constraints. ### Are RoBO and hyperband open source? Yes - both are open-source projects on GitHub (RoBO: BSD-3-Clause, hyperband: Other). ### Where can I find alternatives to RoBO or hyperband? GraphCanon lists graph-backed alternatives at [RoBO alternatives](/tools/automl-robo/alternatives) and [hyperband alternatives](/tools/zygmuntz-hyperband/alternatives) ([RoBO markdown twin](/tools/automl-robo/alternatives.md), [hyperband markdown twin](/tools/zygmuntz-hyperband/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-robo-vs-zygmuntz-hyperband.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, RoBO or hyperband? RoBO: Dormant. hyperband: Dormant. 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 RoBO and hyperband? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [RoBO trust report](/tools/automl-robo/trust); [hyperband trust report](/tools/zygmuntz-hyperband/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=automl-robo`](/api/graphcanon/graph?tool=automl-robo) - 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/_