# Grid search Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/grid-search
> Markdown URL: https://aitinkerers.org/technologies/grid-search.md
> Technology record last updated: 2026-03-05T10:17:05Z
> Generated: 2026-09-22T23:36:15Z

Grid search is the exhaustive, systematic hyperparameter tuning technique: it evaluates every specified combination to find the optimal model configuration.

This method (also called a parameter sweep) defines a discrete grid across a model's hyperparameter space, then trains and validates the model for *every* possible combination. For instance, tuning a Random Forest might require testing three values for `n_estimators` and three values for `max_depth`, resulting in $3 \times 3 = 9$ total model fits. Performance is typically measured using k-fold cross-validation (e.g., $k=5$) to ensure robust results. While guaranteed to find the best combination within the defined grid, Grid Search is computationally expensive, especially as the number of hyperparameters or their value ranges increase.

- Official technology site: https://www.w3schools.com/python/machine_learning_grid_search.asp
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [RFP Summary](https://montreal.aitinkerers.org/talks/rsvp_tlZIcFu8LAY)

Hi - I am JP - I developed an RFP Summary with Gen AI. I have linked a video URL which will give you a good idea of the demo.

- Event context: AI Tinkerers - Montreal Inaugural Meetup (October) — 2024-10-29 — Montreal
- Public talk page: https://montreal.aitinkerers.org/talks/rsvp_tlZIcFu8LAY

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