What is the subset of data called that is used to fine-tune a machine learning model’s parameters during training?

The term used to describe the subset of data that is utilized to fine-tune the parameters of a machine learning model during training is validation data. This subset serves a critical role in the model training process; it helps in assessing the performance of the model while it is being trained, allowing adjustments to be made to improve accuracy and generalizability.

Validation data is separate from training data, which is used to initially train the model, and testing data, which is employed to evaluate the model's performance after training is complete. The primary purpose of validation data is to prevent overfitting, where the model learns noise and details from the training data to the detriment of its ability to generalize to unseen data. By using validation data, practitioners can tweak hyperparameters and make other decisions that enhance the model without compromising its performance on new, unseen data.

The concept of review data isn't standard in the context of machine learning and does not pertain to model training or tuning, making it irrelevant in this context.