Model Selection & Hyperparameter Search - Interactive Visualization

Hyperparameter tuning is one of the highest-leverage activities in applied ML. Grid search exhaustively evaluates every combination - wasteful but thorough. Random search samples hyperparameters independently and is often just as good with far fewer evaluations. Bayesian optimization uses a surrogate model (usually a Gaussian process) to model the validation metric surface and focus evaluations on the most promising regions.

• Watch grid search exhaustively fill the hyperparameter grid vs random search scattering evaluations across the same space
• See why random search works: if only 2 of 10 hyperparameters matter, random search covers those dimensions much more densely
• Observe Bayesian optimization focus evaluations on regions near the current best - each evaluation informs the next
• Understand the acquisition function: expected improvement decides where to evaluate next, trading exploration vs exploitation
• Learn nested cross-validation: the only unbiased way to both tune hyperparameters and estimate generalization error

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