GraphCanon updated Oct 2, 2026 · GitHub synced Oct 2, 2026
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Decision brief
Hyperband optimizes hyperparameters quickly with an efficient bandit-based approach, supporting several models from scikit-learn and polylearn.
Good fit when
- Use Hyperband when you need fast optimization of hyperparameters for classifiers such as gradient boosting or regressors like factorization machines from polylearn.
- Optimize model performance rapidly using the provided meta-classifier/regressor definitions that attempt many models in one run, which can save time on manual search space construction.
Avoid when
- 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.
Observed Jul 17, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (2969d since push)
- As of Oct 2, 2026
- Provenance
- Not a fork · Personal account
- As of Oct 2, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
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Install
pip install hyperbandSimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Hyperband is an optimisation approach for hyperparameter tuning implemented in Python.
Capability facts
- Languages
- python
Source: github.language · Oct 2, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Oct 2, 2026)
```python from hyperband import HyperbandSource link
Tags
README
hyperband ========= Code for tuning hyperparams with Hyperband, adapted from Hyperband: A Novel Bandit Based Approach to Hyperparameter Optimization. defs/ functions and search space definitions for various classifiers defs regression/ the same for regression models common defs.p...
For agents
This page has a .md twin and JSON over the API.