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🎯 Advanced Hyperparameter Concepts: Multi-Objective and Automated Tuning

Watch an automated tuner search a live hyperparameter space in 3D — every trial is plotted by accuracy, model size and training time, and the Pareto-optimal frontier of best trade-offs emerges as random search, Bayesian-style exploitation or evolutionary refinement run.

Machine Learning & Neural Networks3DAdvanced60 FPS

An automated tuner searches a live hyperparameter space in 3D — every trial is plotted by accuracy, model size and training time, and the Pareto-optimal frontier of best trade-offs emerges as random search, Bayesian-style exploitation or evolutionary refinement run.

🔬 What It Demonstrates

Real hyperparameter tuning rarely optimizes just one number. This simulation plots every automated trial in a 3D objective space — model size, accuracy and training time — and highlights the Pareto-optimal frontier: the trials no other trial beats on every objective at once. Watch how the frontier expands and its shape changes as trials accumulate.

🎮 How to Use

Pick a search strategy (random search, Bayesian-style exploitation, or evolutionary mutation of the current frontier), adjust the trial rate, and toggle whether dominated trials and the frontier line are shown. Drag to orbit the objective-space cube and scroll to zoom.

💡 Did You Know?

Real AutoML systems such as Optuna and Google Vizier use exactly this idea — instead of chasing one metric, they maintain a Pareto front of configurations (e.g. accuracy vs. inference latency vs. model size) so an engineer can pick the trade-off that fits their deployment target after the search finishes, rather than before it starts.

⚙ Under the hood

Three.jsHyperparameter TuningMulti-Objective OptimizationPareto FrontAutoMLMachine Learning

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