Training in cancer research often involves the use of machine learning models that can learn from historical data to predict future outcomes. This data can include genomic information, medical imaging, and clinical records. During the training process, the model is fed a dataset where the outcomes are already known. The model then adjusts its algorithms to minimize the difference between its predictions and the actual outcomes.
