Why do We Use both Validation Set and Test Set?

Answer: Using both a validation set and a test set helps ensure the model’s performance is assessed on unseen data, with the validation set guiding the training process and the test set providing an independent evaluation of the model’s generalization ability.

Using both a validation set and a test set serves distinct purposes in the process of developing and evaluating machine learning models.

1. Training and Fine-Tuning:
   • Training Set: This is the data used to train the model. The model learns patterns and relationships within this dataset.
   • Validation Set: During training, a portion of the data is set aside as the validation set. The model is not exposed to this data during training, and it serves as an independent dataset to fine-tune hyperparameters and assess the model’s performance on data it hasn’t seen before.
2. Model Selection and Tuning:
   • Validation Set: After training, the model’s performance on the validation set helps in selecting the best-performing model and tuning hyperparameters. This iterative process ensures the model generalizes well to unseen data.
3. Avoiding Overfitting:
   • Test Set: Once the model is selected and tuned using the validation set, it needs to be evaluated on completely unseen data to ensure it hasn’t overfit to the validation set. The test set provides an unbiased assessment of the model’s generalization to new, previously unseen data.
4. Generalization Performance:
   • Validation Set: Guides the training process, helping to avoid overfitting and ensuring the model is learning relevant patterns.
   • Test Set: Offers an independent benchmark to evaluate the final model’s ability to generalize to new, real-world scenarios.
5. Performance Metrics:
   • Validation Set: Metrics on the validation set are used for comparison during the model development phase to make informed decisions about model architecture and hyperparameters.
   • Test Set: Metrics on the test set provide the final assessment of the model’s performance, reflecting how well it is expected to generalize to new, unseen data in real-world scenarios.
6. Simulating Real-World Conditions:
   • Test Set: By keeping a separate test set, you simulate the real-world scenario where the model encounters entirely new data it hasn’t been exposed to during development. This ensures a more accurate representation of the model’s performance in practical applications.

Conclusion:

In summary, the validation set aids in model development, hyperparameter tuning, and selection, while the test set serves as the ultimate benchmark for evaluating the model’s generalization performance on completely new and unseen data, ensuring robustness and reliability in real-world applications.