What is the primary use of a validation set in machine learning?

The primary use of a validation set in machine learning is to tune the model's hyperparameters and evaluate its performance. When creating a machine learning model, it is crucial to ensure that the model generalizes well to unseen data, not just the data it was trained on.

The validation set serves as an intermediary step between the training dataset and the unseen test dataset. By using the validation set, you can assess how well your model is performing during the training process. This allows for adjustments to be made, specifically in the tuning of hyperparameters—settings that help control the learning process of the model.

For example, if a model is overfitting the training data (where it performs well on the training set but poorly on new, unseen data), the validation set can provide a clear signal for necessary adjustments, such as modifying the complexity of the model or applying regularization techniques.

In summary, the validation set is essential for guiding the learning process, ensuring robustness, and facilitating optimal model performance on new data.