Hyperparameter tuning (also called hyperparameter optimization) refers to the process of finding the optimal set of hyperparameters for a given machine learning algorithm.

Hyperparameter tuning (also called hyperparameter optimization) refers to the process of finding the optimal set of hyperparameters for a given machine learning algorithm. Popular methods for searching for the optimal hyperparameters including: Grid Search, Random Search, Bayesian Optimization, Gradient-Based Optimization, and Early Stopping. Finding the optimal set of hyperparameters can often significantly improve a model's performance.