Cancer research often involves large datasets with numerous features, such as gene expression profiles, protein levels, or clinical data. Identifying the most relevant features is crucial for improving the accuracy of predictive models. RFE helps in reducing the dimensionality of the data, which in turn enhances model performance and interpretability. This is particularly important in cancer, where understanding the role of specific genes or proteins can lead to better diagnostic tools and targeted therapies.
