What is Hyperparameter Tuning?
TL;DR
The process of optimizing model configuration values to maximize performance. Grid search and Bayesian optimization are common methods.
Hyperparameter Tuning: Definition & Explanation
Hyperparameter tuning is the process of optimizing machine learning model hyperparameters — values set before training such as learning rate, number of layers, and regularization strength. Methods include grid search (trying all combinations), random search (sampling randomly), and Bayesian optimization (exploring efficiently based on past results). Libraries like Optuna and Hyperopt are widely used. Proper hyperparameter settings significantly impact model performance, and automated tuning can discover optimal combinations that would be difficult to find manually. AutoML tools also incorporate this functionality.