In the context of clinical studies and the development of predictive models or diagnostic algorithms, data are commonly divided into two distinct subsets: a training set and a test set. The training set consists of a portion of the collected patient samples and serves as the foundation for constructing and refining the predictive model. Researchers use these samples to identify relevant patterns, relationships, and biomarkers—such as the SEPT9 measurement in colorectal cancer (CRC) screening—thereby enabling the algorithm to “learn” how to differentiate between various patient categories. This process may involve selecting informative features, adjusting model parameters, and ensuring that the model fits the biological and clinical data.

Once the model has been fully developed and optimized using the training set, it is then subjected to evaluation on the test set. The test set comprises a separate subset of patient samples that were not involved in the model-building process. By assessing model performance on these previously unseen samples, investigators can determine whether the predictive algorithm maintains robust and generalizable accuracy. Performance metrics derived from the test set—such as sensitivity, specificity, and overall classification accuracy—indicate how well the model is likely to perform in broader clinical practice, beyond the initial conditions under which it was created.

In this manner, the division into training and test sets serves a critical methodological purpose: it helps to prevent overfitting, ensures that the algorithm’s predictive capabilities extend beyond the original dataset, and ultimately enhances the clinical utility and reliability of the predictive model.

An ocean of thanks Olga