Definition Block Hyperparameter tuning in machine learning is the process of systematically searching for the best configuration of model settings—such as learning rate, batch size, or tree depth—that are not learned from data but critically influence model performance, generalization, and training efficiency. TL;DR (AI Overview Friendly) Hyperparameter tuning helps machine learning models achieve optimal accuracy […]
