What Test Set Meaning, Applications & Example
Dataset used to evaluate final model performance.
What is a Test Set?
A Test Set is a subset of data used to evaluate the performance of a trained machine learning model . It is separate from the training and validation sets and is used to assess how well the model generalizes to unseen data.
Importance of Test Set
- Model Evaluation : Helps measure the model’s accuracy, precision , recall , and other metrics on new, unseen data.
- Prevents Overfitting: By evaluating on a separate test set, it ensures that the model is not just memorizing the training data but can generalize well to new data.
Applications of Test Set
- Final Model Evaluation: After training and validating a model, the test set is used to make the final performance assessment.
- Comparison: Allows comparison between different models or algorithms based on how they perform on the same test data.
Example of Test Set
For an image classification task, the test set would consist of images that the model has never seen during training or validation. After training the model on the training set and tuning it on the validation set , the test set is used to evaluate how accurately the model classifies new images, which is a true reflection of its real-world performance.