Health Care Informatics Practice Exam 2025 - Free Practice Questions and Study Guide for Health Informatics Certification

The process that allows for unbiased performance estimates during model development involves holding out data from training. When developing predictive models, it is crucial to evaluate their performance on data that the model has never seen before. By using a subset of the data that is not included in the training process, known as validation or test data, we can obtain an accurate assessment of how well the model is likely to perform when applied to new, unseen cases.

This practice mitigates the risk of overfitting, where a model learns to predict based on the peculiarities of the training data rather than generalizing from patterns. Without holding out a portion of the data, performance estimates may appear artificially high because the model could simply be memorizing or recognizing the specific patterns of the training dataset rather than genuinely predicting outcomes. Thus, by validating performance on data that was specifically excluded from the training process, the estimates become more reliable and unbiased, reflecting the model’s true predictive capabilities in real-world scenarios.