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🎯 Random Search Explained: Hyperparameter Tuning

Why randomly sampling hyperparameters often beats exhaustive grid search, and how to design an effective random search.

AI & Machine Learning3DModerate60 FPS

A purple loss landscape over two hyperparameters, sampled side by side by an evenly spaced grid search and a random search on the same budget — watch which one finds the deeper minimum.

🔬 What It Demonstrates

For a fixed trial budget, grid search only ever probes as many distinct values of each hyperparameter as the grid resolution allows, while random search probes up to one distinct value per trial — a decisive edge when only one hyperparameter really matters.

🎮 How to Use

Set the sample budget and the secondary hyperparameter's influence, then compare the best loss each method finds. Lower the influence toward 0% to simulate low effective dimensionality and watch random search pull ahead.

💡 Did You Know?

Bergstra & Bengio's 2012 study found random search matched grid search's best result using roughly a fifth of the trials on real neural-network tuning tasks.

⚙ Under the hood

Why randomly sampling hyperparameters often beats exhaustive grid search, and how to design an effective random search.

machine learninghyperparameter tuningrandom searchoptimizationalgorithmsdata scienceThree.js

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