Test Set
Test Set is a portion of a dataset reserved for evaluating a trained machine learning model on new, unseen data and measuring how well it generalizes beyond its training data.
What is Test Set?
A test set contains examples that are kept separate from the data used to train the model. After training is complete, the model makes predictions on the test set, and those predictions are compared with the known outcomes using appropriate evaluation metrics.
Why is Test Set Important?
A test set provides an independent measure of model performance and helps reveal whether a model generalizes effectively to unseen data. Keeping the test set separate from training helps prevent overly optimistic performance estimates caused by evaluating the model on data it has already seen.
Common use cases
Test Sets are commonly used in classification, regression, natural language processing, computer vision, recommendation systems, and other machine learning evaluation workflows.