Hyperparameter Tuning
Hyperparameters are the knobs you turn before training starts. Turning them well is the difference between a mediocre model and a great one. Language: Python Implement grid search, random search, and Bayesian optimization from scratch and compare their sample efficiency. Explain why random search outperforms grid search when most hyperparameters have low effective dimensionality. Build a Bayesian optimization loop using a surrogate model and acquisition function to guide the search. Design a hyperparameter tuning strategy that avoids overfitting the validation set through proper cross-validation. Your gradient boosting model has a learning rate, number of trees, max depth, min samples per leaf, subsample ratio, and column sample ratio. That is six hyperparameters. If each has 5 reasonable values, the grid has 5^6 = 15,625 combinations. Training each takes 10 seconds. That is 43 hours of compute to try them all. Grid search is the obvious approach and the worst one at scale. Random search does better with less compute. Bayesian optimization does even better by learning from past evaluations. Knowing which strategy to use, and which hyperparameters actually matter, saves days of wasted GPU time. Parameters are learned during training (weights, biases, split thresholds). Hyperparameters are set before training starts and control how learning happens. Grid search evaluates every combination of specified values. It is exhaustive and easy to understand, but…
Hyperparameter Tuning: Hyperparameters are the knobs you turn before training starts. Turning them well is the difference between a mediocre model and a great…
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