Example Concept: One common approach is to use feature importance scores, which indicate how much each feature contributes to the model's decision-making process. These scores can be derived from the mean decrease in impurity or the mean decrease in accuracy. Additionally, tools like SHAP (SHapley Additive exPlanations) provide a unified measure of feature importance by attributing the prediction of each instance to the features, offering a more granular view of feature contributions. Visualization techniques, such as partial dependence plots, can also illustrate the relationship between features and predicted outcomes, enhancing interpretability.