Description: Hyperparameter tuning is the process of adjusting parameters that are not learned directly during the training of a machine learning model, with the aim of improving its performance. These hyperparameters can include learning rate, number of layers in a neural network, batch size, and others. The proper choice of these values is crucial, as they can significantly influence the model’s ability to generalize to new data. Tuning is often performed using techniques such as grid search, random search, or more advanced methods like Bayesian optimization. This process not only aims to maximize the model’s accuracy but also to minimize overfitting, ensuring that the model fits well to the training data while also performing effectively on unseen data. Therefore, hyperparameter tuning is an essential step in developing robust and reliable models in the field of machine learning and artificial intelligence.
