While feature importance metrics can provide valuable insights into the contribution of variables to the model, relying solely on these metrics for model interpretation has several drawbacks. To mitigate these drawbacks and enhance model explainability, structural engineers can take the following steps:
Model-Agnostic Techniques: Instead of relying on model-specific feature importance metrics, engineers can use model-agnostic techniques like permutation importance or Shapley values. These methods provide a global explanation of feature importance irrespective of the model used, offering a more comprehensive understanding of variable contributions.
Interpretability Metrics: Engineers can complement feature importance metrics with interpretability metrics that assess the model's transparency, sparsity, and sensitivity to perturbations. By evaluating these aspects, engineers can ensure that the model is not only accurate but also interpretable and explainable.
Domain-Specific Interpretation: Incorporating domain-specific knowledge and causal relationships into the interpretation process can help validate the findings from feature importance analysis. By cross-referencing the model's predictions with known structural principles, engineers can verify the relevance and significance of the identified features.
Visualization Techniques: Visualizing the relationships between features and the target variable can provide a more intuitive understanding of the model's behavior. Techniques like partial dependence plots, LIME (Local Interpretable Model-agnostic Explanations), and SHAP (SHapley Additive exPlanations) plots can offer valuable insights into how individual features impact the model's predictions.
By combining feature importance metrics with model-agnostic techniques, interpretability metrics, domain-specific knowledge, and visualization techniques, structural engineers can enhance the interpretability and explainability of their ML models, leading to more reliable and trustworthy deployment in structural engineering applications.