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🔲 Grid Search Explained: Exhaustive Hyperparameter Tuning

Watch grid search exhaustively sweep every hyperparameter combination on a 3D validation-score grid, row by row, and see how resolution and CV folds explode the total model-fit cost.

AI & Machine Learning3DAdvanced60 FPS

A 3D lattice of every learning-rate/regularization combination gets swept in strict nested-loop order, growing colored bars for each validation score and tracking the best combination found so far.

🔬 What It Demonstrates

Grid search tries every point on a fixed lattice exhaustively — cost grows as resolution² per axis and multiplies again by cross-validation folds, while a random-search overlay shows how the same budget looks scattered instead of gridded.

🎮 How to Use

Raise the grid resolution or fold count and watch total model fits explode, adjust scan speed, and toggle the random-search overlay to compare coverage patterns for the same evaluation budget.

💡 Did You Know?

Because grid search cost scales as (grid points)^d, going from 2 to just 4 hyperparameters at 10 values each turns 100 model fits into 10,000 — the "curse of dimensionality" that motivates random and Bayesian search.

⚙ Under the hood

Watch grid search exhaustively sweep every hyperparameter combination on a 3D validation-score grid, row by row, and see how resolution and CV folds explode the total model-fit cost.

machine learninghyperparameter tuninggrid searchoptimizationvalidationmodel fittingThree.js

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install