Home▸AI & Machine Learning▸Evaluating Hyperparameter Tuning Results: Metrics and Validation

📊 Evaluating Hyperparameter Tuning Results

Explore a live 3D validation-loss landscape that shows how learning rate and regularization shape the train/validation gap, and how k-fold cross-validation shrinks score variance.

AI & Machine Learning3DAdvanced60 FPS

A 3D validation-loss landscape over learning rate and regularization strength, with a live train-vs-validation gap and cross-validation fold spread that shrinks as folds and data grow.

🔬 What It Demonstrates

Validation loss forms a bowl-shaped surface with a sweet spot; moving away from it in the direction of low regularization and aggressive learning rates widens the gap between training and validation loss — the signature of overfitting.

🎮 How to Use

Drag the learning rate and regularization sliders to move the marker across the surface. Adjust dataset size and k-fold count to watch the orange cross-validation samples tighten and the confidence interval narrow.

💡 Did You Know?

A single validation split can rank two configurations in the wrong order purely by sampling luck — averaging scores across k folds is one of the simplest ways to get a tuning result you can actually trust.

⚙ Under the hood

Explore a live 3D validation-loss landscape that shows how learning rate and regularization shape the train/validation gap, and how k-fold cross-validation shrinks score variance.

machine learninghyperparameter tuningvalidation losscross validationoptimizationalgorithmsThree.js

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