Conditional Local Importance by Quantile Expectations

2026-06-17 · Source: stat.ML updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, extended

Summary

The paper introduces Conditional Local Importance by QUantile Expectations (CLIQUE), a new model-agnostic method for calculating local variable importance. CLIQUE addresses limitations of existing techniques like SHAP and LIME by accurately capturing locally dependent relationships between variables, reducing bias in regions where variables have no effect, and natively adapting to multi-class classification problems. Unlike permutation-based methods, CLIQUE uses quantile replacements, requiring fewer repetitions for convergence. Simulated experiments with AND Gate, Corners, and Regression Interaction data, alongside real-world applications on Lichen, MNIST, and Concrete datasets, demonstrate CLIQUE's superior ability to emphasize conditional information and yield near-zero importances for irrelevant variables, outperforming SHAP and LIME in these specific scenarios.

Key takeaway

For machine learning engineers and data scientists interpreting complex models, consider integrating CLIQUE into your explainability toolkit. This method offers a more precise understanding of local variable importance by accurately identifying conditional relationships and minimizing false positives where variables lack impact. You can gain deeper insights into model behavior, especially in multi-class scenarios, by focusing on how variables contribute to prediction error rather than just predictions.

Key insights

CLIQUE provides model-agnostic local variable importance, accurately capturing conditional relationships and reducing bias.

Principles

• Local importance should reflect conditional variable effects.
• Quantile replacements reduce variance compared to permutations.
• Importance based on error, not prediction, simplifies multi-class tasks.

Method

CLIQUE computes local importance by comparing cross-validation model error with original CV error after replacing a variable's value with M quantile values from a grid.

In practice

• Use CLIQUE to identify truly influential variables in specific data regions.
• Apply CLIQUE for local importance in multi-class classification problems.
• Employ quantile grids for more stable local importance calculations.

Topics

• Local Variable Importance
• Model Agnostic Explainability
• Conditional Relationships
• Multi-class Classification
• Quantile Expectations
• SHAP and LIME Comparison
• Machine Learning Interpretability

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Data Scientist

Related on AIssential

• Alike Parts: A Feature-Informed Approach to Local and Global Prototype Explanations — Integrating feature importance into prototype selection enhances both local explanations and global prototype diversity without compromising model fidelity.
• When SHAP and LIME Fail: Lessons from Predicting Quality in the Automotive Industry — Explainability tools like SHAP and LIME have critical failure modes that can lead to misleading insights, especially with correlated data.
• Aggregate Models, Not Explanations: Improving Feature Importance Estimation — Ensembling models directly improves feature importance estimation by reducing excess risk, especially for complex ML models.
• Applied Explainability for Large Language Models: A Comparative Study — Gradient-based explainability provides stable LLM insights, while attention is efficient but less aligned with predictions.
• The most important features in generalized additive models might be groups of features — Analyzing feature groups, not just individual features, offers critical insights in interpretable machine learning.
• Bayesian Networks and Markov Networks: An Intuitive Guide to Structured Uncertainty — Graphical models like Bayesian and Markov networks excel at representing structured uncertainty and multi-directional probabilistic reasoning beyond simple prediction.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by stat.ML updates on arXiv.org.