scikit-optimize(skopt)

Overview

Sequential model-based optimization with a scipy.optimize interface. It provides a simple Python API for hyperparameter tuning and black-box optimization using surrogate models. The library is community-maintained and designed to integrate seamlessly with NumPy and SciPy workflows.

Best for

Best for
Python developers who need a simple, scipy-compatible optimizer for moderate-scale hyperparameter or parameter tuning

Use cases

• Hyperparameter tuning for machine learning models
• Black-box optimization of expensive evaluation functions
• Automated parameter search in scientific computing pipelines

Notes

Sequential model-based optimization with a scipy.optimize interface. It provides a simple Python API for hyperparameter tuning and black-box optimization using surrogate models. The library is community-maintained and designed to integrate seamlessly with NumPy and SciPy workflows.

2,826 stars on GitHub. Last updated 2024-02-23. Licensed BSD-3-Clause.

Use cases

• Hyperparameter tuning for machine learning models
• Black-box optimization of expensive evaluation functions
• Automated parameter search in scientific computing pipelines

Pros

• Familiar scipy.optimize interface lowers learning curve
• Lightweight and easy to install with minimal dependencies
• Supports several surrogate models (GP, RF, GBRT) out of the box

Cons

• Sequential nature limits parallel optimization without additional wrappers
• No native support for distributed or cloud-based execution
• Community maintenance may lead to slower issue resolution

Indexed from awesome-llmops and enriched against its public facts.