The primary reason oversampling is vital in cancer research is that it helps improve the accuracy and reliability of predictive models. Many machine learning algorithms perform poorly when trained on imbalanced data, which can result in critical misdiagnoses or overlooked cases. By balancing the dataset, oversampling ensures that the model becomes proficient at identifying both common and rare types of cancer.
