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papersTODAY 04:00 UTC

SAILS: New Method Reveals Functional Form of Feature Interactions in ML Models

A new arXiv paper introduces SAILS, a surrogate-based approach that uses local effect smooths to characterize how features interact inside machine learning models. Unlike prior explanation techniques that only flag or score interactions, or that handle just a narrow set of interaction shapes, SAILS aims to expose the actual functional form of those interactions. The work is posted as a cross-listing on arXiv's cs.AI and cs.LG categories.

arXiv SAILS explainable-ai feature-interactions machine-learning-interpretability surrogate-models

COVERAGE · 2 REPORTS · LINKS GO TO THE ORIGINAL OUTLETS

arXiv cs.AISAILS: Surrogate-based Analysis of Interactions via Local Effect Smooths ↗TODAY 04:00 UTC

arXiv cs.LGSAILS: Surrogate-based Analysis of Interactions via Local Effect Smooths ↗TODAY 04:00 UTC