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🔍 Scikit-Learn Hyperparameter Optimization Implementation

Watch GridSearchCV and RandomizedSearchCV probe a live 3D validation-score surface, sampling points across a hyperparameter grid and comparing how many combinations each strategy needs to find the best model.

AI & Machine Learning3DAdvanced60 FPS

GridSearchCV and RandomizedSearchCV both cross-validate candidate hyperparameter combinations against a hidden generalization landscape — this scene renders that landscape in 3D and drops glowing markers as each strategy evaluates it.

🔬 What It Demonstrates

Grid search lays candidates on an evenly spaced lattice and evaluates all of them; randomized search spends the same budget on random draws, often finding a comparably good peak with far fewer evaluations.

🎮 How to Use

Pick a search strategy and budget, set the number of CV folds to see how estimate noise shrinks, then press Run search and watch candidates populate the surface as the best score and parameters update live.

💡 Did You Know?

Bergstra & Bengio's 2012 paper showed random search reliably beats grid search under a fixed compute budget, because most models are only sensitive to a handful of the hyperparameters being tuned.

⚙ Under the hood

machine learninghyperparameter tuninggrid searchrandomized searchscikit-learnoptimizationmodel selectionThree.js

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