Validation data

Validation data is a subset of a dataset used in machine learning to evaluate a model’s performance during the training phase. Unlike training data, which is used to adjust model parameters, validation data provides a separate set of examples to assess how well the model generalizes to new, unseen data.

The primary purpose of validation data is to fine-tune hyperparameters, such as learning rates, number of layers, or other model settings. By testing different configurations on the validation set, data scientists can select the version of the model that performs best without using the final test set. This process also helps prevent overfitting, where a model performs well on training data but fails to generalize to new inputs.