Validation sets are essential in preventing overfitting in machine learning by ensuring robust model performance on new data.
Using a validation set effectively during model training helps fine-tune the model without prematurely looking at the test set, thus promoting ethical AI practices.
A validation set safeguards against overfitting, which may lead to models performing poorly when deployed in real-world scenarios.
Validation sets play a crucial role in building trustworthy AI by providing a reliable testing mechanism and preventing overfitting issues.