Interpretability can be achieved through various methods:
Feature Importance: Identifying which features or biomarkers are most significant in predicting cancer outcomes. Model Transparency: Using simpler models like decision trees or linear models that are inherently interpretable. Post-Hoc Interpretability: Applying techniques like SHAP (Shapley Additive Explanations) values to interpret complex models. Visualizations: Using graphs and charts to make complex data more understandable.
