Test Set

In machine learning, a test set is a distinct subset of a larger dataset, used solely to provide an unbiased evaluation of a model's final performance after it has been fully trained and optimized. Its primary purpose is to assess the model's generalization capability – how well it performs on new, unseen data, simulating real-world scenarios. Crucially, the test set is never used during the model's training phase or for hyperparameter tuning, preventing data leakage and ensuring an honest and objective measure of the model's efficacy.