General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models (2007.04131v2)

Abstract: An increasing number of model-agnostic interpretation techniques for ML models such as partial dependence plots (PDP), permutation feature importance (PFI) and Shapley values provide insightful model interpretations, but can lead to wrong conclusions if applied incorrectly. We highlight many general pitfalls of ML model interpretation, such as using interpretation techniques in the wrong context, interpreting models that do not generalize well, ignoring feature dependencies, interactions, uncertainty estimates and issues in high-dimensional settings, or making unjustified causal interpretations, and illustrate them with examples. We focus on pitfalls for global methods that describe the average model behavior, but many pitfalls also apply to local methods that explain individual predictions. Our paper addresses ML practitioners by raising awareness of pitfalls and identifying solutions for correct model interpretation, but also addresses ML researchers by discussing open issues for further research.

• The paper reveals that misapplying interpretation methods can lead to misleading insights, stressing the importance of validating model generalization.
• The paper demonstrates that ignoring feature dependencies and high-dimensional challenges may skew results, urging nuanced statistical analysis.
• The paper underscores that unnecessary model complexity obscures clarity, recommending the use of simpler models and refined strategies for accurate interpretation.

Overview of Model-Agnostic Interpretation Pitfalls for Machine Learning Models

The paper "General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models" presents a comprehensive examination of challenges faced by those deploying model-agnostic interpretation techniques in machine learning contexts. The authors draw attention to various pitfalls that can lead to misconceptions or incorrect conclusions, particularly if the methodologies are misapplied or if the underlying assumptions are not attentively considered. The precise intent of such methods is to illuminate the behavior of complex models, but the complexities and nuances involved can lead to erroneous interpretations without careful application and understanding.

A key focus is given to the common misapplication of model-agnostic interpretation techniques, such as partial dependence plots (PDP), permutation feature importance (PFI), and Shapley values. While these techniques hold potential for revealing insights into model structures, they can lead to errors if not synchronized with the correct interpretative context or if used on models that do not generalize well, neglect feature dependencies, or if applied without regard for interaction and uncertainty.

Several specific pitfalls are highlighted:

1. Bad Model Generalization: Interpreting models that do not generalize well can lead to irrelevant or misleading insights into feature interactions or importance. Validating model performance on out-of-sample data is essential.
2. Feature Dependence: Dependent features can skew model interpretation, leading to unrealistic data points during model probing processes. It is critical to account for feature dependencies using statistical measures that capture the full breadth of potential dependencies, including non-linear associations.
3. Unnecessary Complexity: Using opaque ML models when simpler ones suffice can obscure interpretative clarity. Researchers are encouraged to begin with simpler, more interpretable models, only escalating to more complex structures as needed for performance or predictive accuracy.
4. Interaction Effects: Interpreting average feature effects where interactions among features exist may mask significant information, misleading conclusions about feature importance or effect. More nuanced interpretative strategies such as ICE curves provide better interaction visibility.
5. Causal Misinterpretation: Interpreting model outcomes with causal intent demands rigorous alignment with assumptions about causal structures, which ML models might not be designed to reflect. A clear distinction between correlation and causation must be made.
6. High Dimensionality Challenges: The interpretability of model outputs can become obfuscated in high-dimensional settings, and practitioners should consider dimensionality reduction or grouping of features to manage computational and human interpretation complexity effectively.

The implications of these findings span both practical and theoretical domains, urging deeper investigation into interpretation techniques that might better account for the nuances of machine learning models. Essential future directions pinpoint the need for development in diagnostic tools to ensure reliability in interpretations, enhanced measures for dependent feature analysis, and more robust frameworks for causal interpretations.

In sum, while model-agnostic interpretation techniques offer unparalleled utility in harnessing and understanding the predictive might of machine learning models, practitioners must navigate an intricate space fraught with challenges and subtleties to attain accurate and meaningful interpretations. The paper posits a pressing call to action for further research devoted to learning how best to apply these powerful methodologies in increasingly complex and impactful applications.