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🗺️ Search Space Analysis: Mathematics of Hyperparameter Spaces

Learn about search space analysis in hyperparameter optimization. Understand dimensionality, topology, and structure of hyperparameter spaces.

Mathematics3DAdvanced60 FPS

A 3D loss landscape shaped by two hyperparameters, explored live by grid search, random search, Latin hypercube sampling, or a Bayesian-guided sampler that homes in on the best region found so far.

🔬 What It Demonstrates

The surface's peaks, valleys and ridges are the topology of a hyperparameter search space; its ruggedness controls how many local minima exist and how easily a search strategy can get stuck in one.

🎮 How to Use

Pick a search strategy and sample budget, then watch colored spheres populate the landscape as each configuration is evaluated. The glowing marker tracks the best loss found; the purple trail shows the Bayesian sampler's exploration path.

💡 Did You Know?

Grid search cost grows exponentially with the number of hyperparameters, while random and Bayesian methods degrade far more gracefully — one reason modern AutoML tools default to smarter samplers.

⚙ Under the hood

Learn about search space analysis in hyperparameter optimization. Understand dimensionality, topology, and structure of hyperparameter spaces.

hyperparametersoptimizationdimensionality reductionspace analysismathematicsalgorithmsThree.js

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