Hyperparameter tuning is the process of selecting the best hyperparameters for a machine learning model, which are configuration settings that are not learned from the training data, but instead set by the user before training the model. These parameters have a significant impact on the performance of the model and can include the learning rate, number of hidden layers, number of neurons in each layer, activation functions, regularization strength, and more. The goal of hyperparameter tuning is to find the best parameter values that give the highest accuracy or lowest error on the validation or test dataset. Grid search, random search, and Bayesian optimization are common techniques used for hyperparameter tuning.
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