Machine learning evaluation dataset · BEGINNER LESSON
Test set
A test set is a separate group of data samples kept out of model design and training so a model’s performance can be measured on unseen data. It is used to estimate out-of-sample error, meaning how well the model is expected to do on new data after deployment.
What it is and what is measured
In machine learning, a test set is a collection of samples that are not used to train the model. Its purpose is to measure performance on unseen data and estimate out-of-sample error, which is the expected error on future data. Common performance measures can include sensitivity, specificity, or ROC-AUC, depending on the task. A good test set should represent the data the model will face after deployment.
[1] This report is the result of studies performed at Lincoln … [2] Dividing the original dataset | Machine Learning [3] Recommendations on compiling test datasets for evaluating ...
How a test set is used
The model is built on training data, and a separate validation set may be used during development to compare model choices. The test set should be kept separate from both training and design decisions so the final evaluation stays honest and unbiased. Reusing the test set during development can cause the model or the developer to adapt to it, which can make the measured performance look better than the true performance on new data.
[1] This report is the result of studies performed at Lincoln … [2] Dividing the original dataset | Machine Learning [3] Recommendations on compiling test datasets for evaluating ...
Why design matters
A test set should be large enough that random sampling does not create large swings in the measured result. It should also be diverse and representative of the target population, meaning the real group of cases the model is meant to handle. In some settings, relevant subgroups should be checked separately to look for hidden stratification, which means a model may perform differently across important subsets such as equipment types or patient groups.
[1] This report is the result of studies performed at Lincoln … [3] Recommendations on compiling test datasets for evaluating ...
Limitations and common problems
Test sets are samples, not the full population, so every test result has sampling error. If the test set is too small, the estimate can be unstable. If training and test data share duplicates, spurious correlations, or come from the same limited source, the test result can be too optimistic. A test set also wears out if it is used repeatedly for decisions about model changes or hyperparameters, which are model settings chosen during development.
[2] Dividing the original dataset | Machine Learning [3] Recommendations on compiling test datasets for evaluating ...
SEE THE IDEA
How a test set works in model evaluation
Used to fit the model parameters.
Used during development to compare choices and tune settings.
Kept separate to estimate performance on unseen data.
The future cases the model should handle after deployment.
Training builds the model, validation helps choose among options, and the test set gives the final held-out estimate of performance on new data.
A test set sits outside the development loop so evaluation is less biased by training decisions.
TRY IT YOURSELF
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Sources and further reading
This general lesson was generated from public source material. It explains a concept; it does not interpret a private paper or provide medical advice.