Home▸AI & Machine Learning▸Bayesian Optimization for Hyperparameter Tuning

🎯 Bayesian Optimization for Hyperparameter Tuning Lab

A 3D lab where a Gaussian-process surrogate model hunts for the best hyperparameters over an unknown validation-accuracy landscape, guided by an acquisition function you tune live.

AI & Machine Learning3DAdvanced60 FPS

A Gaussian-process surrogate model searches a hidden hyperparameter landscape trial by trial, using an acquisition function to trade off exploring uncertain regions against exploiting the best result found so far.

🔬 What It Demonstrates

The colored surface is the surrogate's posterior mean; the pale wireframe shell above it is its uncertainty envelope. Together they show how Bayesian optimization models both "what looks good" and "how sure are we" before spending another expensive trial.

🎮 How to Use

Raise exploration (κ) to send trials into uncertain territory, or lower it to chase the current best. Adjust the GP lengthscale to see how smoothness assumptions reshape the fit, then click "Sample suggested point" repeatedly to watch the surrogate converge on the true optimum.

💡 Did You Know?

Bayesian optimization often needs only a few dozen trials to approach the best hyperparameter setting in a space where grid search would require thousands of full training runs.

⚙ Under the hood

A 3D lab where a Gaussian-process surrogate model hunts for the best hyperparameters over an unknown validation-accuracy landscape, guided by an acquisition function you tune live.

machine learningoptimizationhyperparametersbayesian optimizationsurrogate modelstuningThree.js

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