Our verdict
We have not reviewed Spearmint yet
No one at Enterprise DNA has checked this listing against its sources. The description comes from public listings gathered automatically. Any fact below shows where it came from and when it was last checked.
From the listing
About Spearmint
Spearmint is a Python library for Bayesian optimization, enabling efficient hyperparameter tuning of machine learning models. It uses Gaussian processes to model the objective function and selects the next parameters to evaluate via expected improvement. The codebase is designed for sequential optimization where each evaluation is expensive.
Best for
Researchers and engineers needing a reliable Bayesian optimization library for expensive black-box functions
Use cases
- Tuning hyperparameters of deep learning models
- Optimizing simulation parameters with costly evaluations
- Automating experiment design for scientific computing
Notes
1,568 stars on GitHub. Last updated 2019-12-27.
Pros
- Proven Bayesian optimization algorithm with solid theoretical foundation
- Well-documented codebase with 1.5k+ GitHub stars
- Handles noisy objective functions gracefully
Cons
- Limited to sequential optimization, no parallel evaluation support
- Requires manual integration into existing workflows
- Not actively maintained; last updates are several years old
Key facts
What we have checked
- GitHub stars
- 1,573
Each value shows when we last confirmed it and where it came from. Values we have not confirmed are left out, never guessed.
Stars and usage
How Spearmint is growing
1,568 to 1,573
Sources
Where this comes from
- github api via nightly crawl, recovered from git history 20e874270887c1008f860ee0198e461a23c9e438Registry
A source shows what was published when we checked it. It does not certify security, performance or a working installation. Read our method.
Changelog
What changed
No changes recorded yet. Releases, price changes and deprecations appear here, with their dates, once we track them for this record.
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