Tuning with hyperparameters
Hyperparameter tuning is the process of systematically searching for and selecting the optimal values for the hyperparameters of a machine learning model. Unlike model parameters, which are learned from data during training, hyperparameters are determined by the practitioner and define characteristics such as the complexity of the model, the learning rate, regularization strength, and more. The goal of hyperparameter tuning is to identify the hyperparameter values that lead to the best possible model performance on unseen data.
Hyperparameter tuning involves experimenting with different values for each hyperparameter and evaluating the model’s performance using appropriate evaluation metrics, often on a validation set. This process can be guided by different strategies, such as grid search, random search, or more advanced techniques such as Bayesian optimization.
Grid search
Grid search is a systematic approach to hyperparameter tuning. It...
