There are several methods to determine feature importance, including:
Decision Trees and Random Forests: These algorithms provide a straightforward way to calculate feature importance based on the frequency and impact of splits involving each feature. SHAP (SHapley Additive exPlanations): This method offers a unified measure of feature importance by considering the contribution of each feature across different model predictions. LIME (Local Interpretable Model-agnostic Explanations): This approach approximates the model locally to understand the influence of each feature on specific predictions.
