hyperparameter-optimization
hyperparameter-optimization is a Time series & forecasting skill for AI agents, published by skforecast in the skforecast repository on GitHub. Skillspace rates hyperparameter-optimization D (64/100) and it passed the safety scan; the rating is based on static analysis only.
Optimizes forecaster hyperparameters using grid search, random search, or Bayesian search (Optuna). Covers single-series and multi-series search, cross-validation configuration, and search space definition. Use when the user wants to find the best model configuration.
- Grade
- D
- Quality
- 64 / 100
- Safety
- Passed
- GitHub stars
- 1,512
Unproven: rated by static analysis only, not yet compared head-to-head.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 82 / 100
- Maturity
- Structured: ships references or scripts
- Security scan
- No issues found
- License
- BSD-3-Clause
- Adoption
- Adopted by 0 other repositories, in 0 collections
- Scope
- Vendor-specific: built around one product. Hyperparameter search API of the public skforecast library
Not checked yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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