# scikit-optimize Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/scikit-optimize
> Markdown URL: https://aitinkerers.org/technologies/scikit-optimize.md
> Technology record last updated: 2026-02-25T10:18:34Z
> Generated: 2026-09-21T15:54:14Z

A Python library for sequential model-based optimization designed to minimize expensive, noisy black-box functions.

Scikit-optimize (skopt) streamlines hyperparameter tuning through Bayesian optimization techniques like Gaussian processes and decision trees. It serves as a direct upgrade to traditional grid or random search methods by using past evaluation results to inform future trials. The library integrates seamlessly with scikit-learn via the BayesSearchCV class: users replace standard search CVs with a few lines of code to find optimal parameters in fewer iterations. It supports complex search spaces (including categorical and integer dimensions) and provides built-in visualization tools to analyze convergence and objective landscapes.

- Official technology site: https://scikit-optimize.github.io/stable/
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [AutoRAG](https://berlin.aitinkerers.org/talks/rsvp_4eSc1tq148s)

Automatic hyperparameter optimization of your retrieval augmented generation pipeline. Stop fiddling around manually with chunk size, top k and so on. Let the machine do the tuning of your retrieval settings. Inspired by hyperparameter optimization in conventional ML.

- Event context: AI Tinkerers Berlin - March 21 — 2024-03-21 — Berlin
- Public talk page: https://berlin.aitinkerers.org/talks/rsvp_4eSc1tq148s

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