Hyperparameter Tuning

Hyperparameter tuning is the process of choosing settings that govern a model’s architecture or learning procedure but are not learned directly as model weights. Examples include learning rate, tree depth, regularization strength, batch size, and the number of layers. Practitioners may use manual experiments, grid search, random search, Bayesian optimization, or automated schedulers. Candidate configurations are compared on validation data rather than the final test set. Tuning can substantially improve results, but excessive searching may overfit the validation benchmark and consume large amounts of compute. A sound process records experiments, controls randomness, sets resource limits, and confirms the selected configuration on untouched data.