What is the function of an evaluation dataset in model training?

In the context of model training, an evaluation dataset serves a crucial role in assessing the performance of the model. After the model has been trained on a training dataset, it's important to gauge how well it can perform on unseen data, which is where the evaluation dataset comes into play. By using this separate set of data that the model has not encountered during training, practitioners can determine the model's accuracy, precision, recall, or other performance metrics.

This evaluation allows for insights into how the model generalizes to new data, identifying potential overfitting where the model performs well on training data but poorly on unseen data. The evaluation results can guide adjustments in model parameters, hyperparameters, or even lead to redesigning aspects of the model to enhance its predictive capabilities. In summary, the primary function of an evaluation dataset is to enable an objective measurement of a trained model's performance on data that it was not originally trained on, ensuring its reliability and effectiveness in real-world applications.