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Assessing high-order effects in feature importance via predictability decomposition.

Marlis Ontivero-Ortega1, Luca Faes2,3, Jesus M Cortes4,5,6,7

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Summary
This summary is machine-generated.

We introduce an adaptive Leave One Covariate Out (LOCO) method to quantify feature importance interactions. This approach decomposes cooperative effects, revealing redundancy and synergy in machine learning models.

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Statistical Modeling

Background:

  • Understanding multivariate interactions is crucial for accurate statistical modeling.
  • Feature importance techniques in explainable AI often overlook complex interdependencies.
  • Existing methods for quantifying feature interactions can be computationally intensive and lack nuance.

Purpose of the Study:

  • To develop a novel approach for quantifying cooperative effects in feature importance.
  • To introduce an adaptive version of the Leave One Covariate Out (LOCO) metric.
  • To disentangle high-order interactions, including redundancy and synergy, in regression problems.

Main Methods:

  • Proposed an adaptive Leave One Covariate Out (LOCO) method.
  • Identified feature subsets that maximize and minimize LOCO.
  • Decomposed LOCO into two-body and higher-order (redundant, synergistic) components.

Main Results:

  • The adaptive LOCO method effectively quantifies cooperative effects in feature importance.
  • Successfully decomposed feature importance into synergistic and redundant components.
  • Demonstrated effectiveness on benchmark datasets for wine quality and particle discrimination.

Conclusions:

  • The proposed adaptive LOCO method offers a more nuanced understanding of feature interactions.
  • This technique enhances explainability in artificial intelligence by detailing higher-order effects.
  • The method provides valuable insights into feature contributions in complex regression tasks.