What is hyperparameter tuning in machine learning?

Hyperparameter tuning in machine learning refers to the technique of optimizing the parameters of an algorithm, known as hyperparameters, which are not learned from the training process but are set prior to it. These hyperparameters dictate the structure and behavior of the model during training.

In this context, adjusting the hyperparameters can significantly impact the model's performance. Examples of hyperparameters include learning rate, number of epochs, batch size, and specific settings tailored to a particular algorithm, such as the number of hidden layers in a neural network.

Through strategies such as grid search, random search, or more advanced optimization techniques like Bayesian optimization, practitioners can experiment with different combinations of hyperparameters to identify the setting that yields the best performance on validation data. This optimization process is crucial as it directly affects how well the model generalizes to unseen data, aiming to strike a balance between bias and variance. By selecting the optimal hyperparameters, one can improve the predictive performance and robustness of the machine learning model.