Description: Hyperparameter constraints are limits imposed on the values that hyperparameters can take during the optimization process in machine learning models. These hyperparameters are configurations that are not learned directly from the model but must be set before training. Constraints can take various forms, such as upper and lower bounds, or even more complex restrictions that define relationships between multiple hyperparameters. For example, in a machine learning model, the learning rate could be restricted to a specific range, or the number of layers and neurons could be limited based on the complexity of the problem. These constraints are crucial because they help guide the search process toward configurations that are more likely to produce an effective model, avoiding combinations that could lead to overfitting or poor performance. Additionally, by setting limits, the search space can be reduced, which in turn can speed up the optimization process and make it more efficient. In summary, hyperparameter constraints are an essential tool in optimizing machine learning models, as they allow for more precise control over the training process and improve the quality of the results obtained.
