Once the model has been trained using the training dataset, it is essential to test its performance with a test dataset. This dataset evaluates the performance of the model and verifies that it can generalize well with an unseen data sample. The test dataset is another subsample of the original data, often containing similar characteristics and having a proportional probability distribution to the training set. It serves as a benchmark for evaluating model performance once the training stage has been completed. Typically, the test dataset comprises 20-25% of all original data used in a Machine Learning project.
At this stage, the testing accuracy can be compared to the accuracy obtained on the training set; if the accuracy of the training data is significantly greater than that of the testing data, then this indicates overfitting. As such, it is critical that the test data is representative of the original dataset and is sufficiently large enough to generate accurate predictions.
What is Test Data Used For?
- Test data is essential for software development, as it helps ensure the software’s reliability, efficiency, and error-free operation.
- Banks use test data to monitor customer transactions and detect suspicious activity, such as anomalous purchases or withdrawals.
- Companies can leverage test data to gain insights about customers, drive product development, optimize marketing strategies and boost sales.
- In healthcare, test data is used to develop tests, drugs, and treatment protocols with better efficacy and to analyze patient data to recognize patterns to make informed decisions regarding patient care.
- Test data is also used in education to evaluate student performance, assess educational programs, generate standardized tests, and provide feedback to teachers and students on areas needing improvement.
