Accuracy is not the best evaluation metric for a cancer prediction problem due to its potential to be misleading in imbalanced datasets where the classes are unevenly distributed.
Alternative evaluation metrics such as precision, recall, F1-score, area under the ROC curve (AUC-ROC), or area under the precision-recall curve (AUC-PR) provide a more comprehensive understanding of model performance across different class distributions.
