Valideringsdatasættet

Valideringsdatasættet, often referred to as the validation set, is a crucial component in the development and evaluation of machine learning models. It serves as an intermediate dataset between the training set and the test set. The primary purpose of the validation set is to tune hyperparameters and make architectural decisions for a model. During the training phase, a model learns patterns from the training data. However, simply evaluating the model on the same training data would lead to an overly optimistic and inaccurate assessment of its performance, as the model might have simply memorized the training examples.

The validation set is used to assess how well the model generalizes to unseen data *during* the development process. By repeatedly training the model on the training set and evaluating its performance on the validation set, developers can adjust parameters like learning rate, regularization strength, or the number of layers in a neural network. This iterative process helps prevent overfitting, where a model performs exceptionally well on the training data but poorly on new data. Once the model's hyperparameters are finalized using the validation set, the model is then evaluated one last time on a completely separate test set. This final evaluation provides an unbiased estimate of the model's performance in a real-world scenario.