Home▸Machine Learning & Neural Networks▸Grid vs Random vs Bayesian Search (2D)

Grid Search vs Random Search vs Bayesian Optimization (2D)

Interactive 2D hyperparameter search: grid search, random search and a simplified Bayesian optimizer race across a hidden multi-modal validation-accuracy landscape, with a live best-accuracy-vs-evaluations chart showing which strategy actually wins per unit of budget.

Machine Learning & Neural Networks2DModerate60 FPS📱 Mobile-adapted⇄ 3D version

This 2D hyperparameter-tuning lab plots two tunable parameters on the x and y axes over a hidden, smooth, multi-modal validation-accuracy landscape, then races three real search strategies against it at once: grid search sweeps a fixed lattice, random search samples uniformly, and a simplified Bayesian optimizer concentrates its search around the best point found so far while still occasionally exploring. A live best-accuracy-vs-evaluations chart shows which strategy actually converges faster for a given budget — the same trade-off real AutoML pipelines face when choosing how to tune a model.

⚙ Under the hood

machine learninghyperparameter tuninggrid searchrandom searchbayesian optimizationAutoML2D

2D · HTML5 Canvas 2D · 60 FPS target · runs fully client-side, no install