There are various methods to enhance the interpretability of models in cancer research. One approach is the use of simpler models, such as decision trees or linear regression, which are inherently more interpretable. However, these models may not capture the complexity of biological data as well as more advanced models. Another method involves post-hoc techniques such as SHAP (SHapley Additive exPlanations) or LIME (Local Interpretable Model-agnostic Explanations), which provide explanations for individual predictions made by complex models. These techniques help in visualizing which features contribute most to the prediction.
