Evaluation involves assessing the performance of the trained model to determine its accuracy and reliability. Various metrics can be used for this purpose, including precision, recall, and F1-score. Additionally, cross-validation techniques are often employed to ensure that the model performs well on unseen data.
In the context of cancer, evaluation is crucial because incorrect predictions can have serious consequences. For instance, a false negative (failing to detect cancer when it is present) could delay treatment and worsen the prognosis. Therefore, rigorous evaluation is essential for ensuring that the models are both sensitive and specific.
