iLOCO: Distribution-Free Inference for Feature Interactions

• URL: http://arxiv.org/abs/2502.06661v1
• Date: Mon, 10 Feb 2025 16:49:46 GMT
• Title: iLOCO: Distribution-Free Inference for Feature Interactions
• Authors: Camille Little, Lili Zheng, Genevera Allen,
• Abstract summary: We propose a new model-agnostic metric for measuring the importance of higher-order feature interactions. We develop distribution-free and assumption-light confidence intervals for our iLOCO metric. We validate our iLOCO metric and our confidence intervals on both synthetic and real data sets.
• Score: 4.56754610152086
• License:
• Abstract: Feature importance measures are widely studied and are essential for understanding model behavior, guiding feature selection, and enhancing interpretability. However, many machine learning fitted models involve complex, higher-order interactions between features. Existing feature importance metrics fail to capture these higher-order effects while existing interaction metrics often suffer from limited applicability or excessive computation; no methods exist to conduct statistical inference for feature interactions. To bridge this gap, we first propose a new model-agnostic metric, interaction Leave-One-Covariate-Out iLOCO, for measuring the importance of higher-order feature interactions. Next, we leverage recent advances in LOCO inference to develop distribution-free and assumption-light confidence intervals for our iLOCO metric. To address computational challenges, we also introduce an ensemble learning method for calculating the iLOCO metric and confidence intervals that we show is both computationally and statistically efficient. We validate our iLOCO metric and our confidence intervals on both synthetic and real data sets, showing that our approach outperforms existing methods and provides the first inferential approach to detecting feature interactions.

Related papers

• Targeted Learning for Variable Importance [23.428985354228672]
  variable importance is one of the most widely used measures for interpreting machine learning. We propose a novel method by employing the targeted learning (TL) framework, designed to enhance robustness in inference for variable importance metrics. We show that it (i) retains the efficiency of traditional methods, (ii) maintains comparable computational complexity, and (iii) delivers improved accuracy, especially in finite sample contexts.
  arXiv  Detail & Related papers  (2024-11-04T16:14:45Z)
• Exploiting Structure in Offline Multi-Agent RL: The Benefits of Low Interaction Rank [52.831993899183416]
  We introduce a structural assumption -- the interaction rank -- and establish that functions with low interaction rank are significantly more robust to distribution shift compared to general ones. We demonstrate that utilizing function classes with low interaction rank, when combined with regularization and no-regret learning, admits decentralized, computationally and statistically efficient learning in offline MARL.
  arXiv  Detail & Related papers  (2024-10-01T22:16:22Z)
• Enhancing Interaction Modeling with Agent Selection and Physical Coefficient for Trajectory Prediction [1.6954753390775528]
  We present ASPILin, which manually selects interacting agents and calculates their correlations instead of attention scores. Remarkably, experiments conducted on the INTERACTION, highD, and CitySim datasets demonstrate that our method is efficient and straightforward.
  arXiv  Detail & Related papers  (2024-05-21T18:45:18Z)
• AntEval: Evaluation of Social Interaction Competencies in LLM-Driven Agents [65.16893197330589]
  Large Language Models (LLMs) have demonstrated their ability to replicate human behaviors across a wide range of scenarios. However, their capability in handling complex, multi-character social interactions has yet to be fully explored. We introduce the Multi-Agent Interaction Evaluation Framework (AntEval), encompassing a novel interaction framework and evaluation methods.
  arXiv  Detail & Related papers  (2024-01-12T11:18:00Z)
• IPCC-TP: Utilizing Incremental Pearson Correlation Coefficient for Joint Multi-Agent Trajectory Prediction [73.25645602768158]
  IPCC-TP is a novel relevance-aware module based on Incremental Pearson Correlation Coefficient to improve multi-agent interaction modeling. Our module can be conveniently embedded into existing multi-agent prediction methods to extend original motion distribution decoders.
  arXiv  Detail & Related papers  (2023-03-01T15:16:56Z)
• Variable Importance Matching for Causal Inference [73.25504313552516]
  We describe a general framework called Model-to-Match that achieves these goals. Model-to-Match uses variable importance measurements to construct a distance metric. We operationalize the Model-to-Match framework with LASSO.
  arXiv  Detail & Related papers  (2023-02-23T00:43:03Z)
• Inference for Interpretable Machine Learning: Fast, Model-Agnostic Confidence Intervals for Feature Importance [1.2891210250935146]
  We develop confidence intervals for a widely-used form of machine learning interpretation: feature importance. We do so by leveraging a form of random observation and feature subsampling called minipatch ensembles. Our approach is fast as computations needed for inference come nearly for free as part of the ensemble learning process.
  arXiv  Detail & Related papers  (2022-06-05T03:14:48Z)
• Stabilizing Q-learning with Linear Architectures for Provably Efficient Learning [53.17258888552998]
  This work proposes an exploration variant of the basic $Q$-learning protocol with linear function approximation. We show that the performance of the algorithm degrades very gracefully under a novel and more permissive notion of approximation error.
  arXiv  Detail & Related papers  (2022-06-01T23:26:51Z)
• Accurate and Robust Feature Importance Estimation under Distribution Shifts [49.58991359544005]
  PRoFILE is a novel feature importance estimation method. We show significant improvements over state-of-the-art approaches, both in terms of fidelity and robustness.
  arXiv  Detail & Related papers  (2020-09-30T05:29:01Z)
• Robust Unsupervised Learning of Temporal Dynamic Interactions [21.928675010305543]
  In this paper we introduce a model-free metric based on the Procrustes distance for robust representation learning of interactions. We also introduce an optimal transport based distance metric for comparing between distributions of interaction primitives. Their usefulness will be demonstrated in unsupervised learning of vehicle-to-vechicle interactions extracted from the Safety Pilot database.
  arXiv  Detail & Related papers  (2020-06-18T02:39:45Z)
• Understanding Global Feature Contributions With Additive Importance Measures [14.50261153230204]
  We explore the perspective of defining feature importance through the predictive power associated with each feature. We introduce two notions of predictive power (model-based and universal) and formalize this approach with a framework of additive importance measures. We then propose SAGE, a model-agnostic method that quantifies predictive power while accounting for feature interactions.
  arXiv  Detail & Related papers  (2020-04-01T19:17:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.