REPID: Regional Effect Plots with implicit Interaction Detection

• URL: http://arxiv.org/abs/2202.07254v1
• Date: Tue, 15 Feb 2022 08:54:00 GMT
• Title: REPID: Regional Effect Plots with implicit Interaction Detection
• Authors: Julia Herbinger and Bernd Bischl and Giuseppe Casalicchio
• Abstract summary: Interpretable machine learning methods visualize marginal feature effects but may lead to misleading interpretations when feature interactions are present. We introduce implicit interaction detection, a novel framework to detect interactions between a feature of interest and other features. The framework also quantifies the strength of interactions and provides interpretable and distinct regions in which feature effects can be interpreted more reliably.
• Score: 0.9023847175654603
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine learning models can automatically learn complex relationships, such as non-linear and interaction effects. Interpretable machine learning methods such as partial dependence plots visualize marginal feature effects but may lead to misleading interpretations when feature interactions are present. Hence, employing additional methods that can detect and measure the strength of interactions is paramount to better understand the inner workings of machine learning models. We demonstrate several drawbacks of existing global interaction detection approaches, characterize them theoretically, and evaluate them empirically. Furthermore, we introduce regional effect plots with implicit interaction detection, a novel framework to detect interactions between a feature of interest and other features. The framework also quantifies the strength of interactions and provides interpretable and distinct regions in which feature effects can be interpreted more reliably, as they are less confounded by interactions. We prove the theoretical eligibility of our method and show its applicability on various simulation and real-world examples.

Related papers

• Visual-Geometric Collaborative Guidance for Affordance Learning [63.038406948791454]
  We propose a visual-geometric collaborative guided affordance learning network that incorporates visual and geometric cues. Our method outperforms the representative models regarding objective metrics and visual quality.
  arXiv  Detail & Related papers  (2024-10-15T07:35:51Z)
• Inferring Relational Potentials in Interacting Systems [56.498417950856904]
  We propose Neural Interaction Inference with Potentials (NIIP) as an alternative approach to discover such interactions. NIIP assigns low energy to the subset of trajectories which respect the relational constraints observed. It allows trajectory manipulation, such as interchanging interaction types across separately trained models, as well as trajectory forecasting.
  arXiv  Detail & Related papers  (2023-10-23T00:44:17Z)
• Re-mine, Learn and Reason: Exploring the Cross-modal Semantic Correlations for Language-guided HOI detection [57.13665112065285]
  Human-Object Interaction (HOI) detection is a challenging computer vision task. We present a framework that enhances HOI detection by incorporating structured text knowledge.
  arXiv  Detail & Related papers  (2023-07-25T14:20:52Z)
• On the Joint Interaction of Models, Data, and Features [82.60073661644435]
  We introduce a new tool, the interaction tensor, for empirically analyzing the interaction between data and model through features. Based on these observations, we propose a conceptual framework for feature learning. Under this framework, the expected accuracy for a single hypothesis and agreement for a pair of hypotheses can both be derived in closed-form.
  arXiv  Detail & Related papers  (2023-06-07T21:35:26Z)
• Decomposing Global Feature Effects Based on Feature Interactions [10.874932625841257]
  Generalized additive decomposition of global effects (GADGET) is a new framework for finding interpretable regions in the feature space. We provide a mathematical foundation of the framework and show that it is applicable to the most popular methods to visualize marginal feature effects. We empirically evaluate the theoretical characteristics of the proposed methods based on various feature effect methods in different experimental settings.
  arXiv  Detail & Related papers  (2023-06-01T10:51:12Z)
• Collective Relational Inference for learning heterogeneous interactions [8.215734914005845]
  We propose a novel probabilistic method for relational inference, which possesses two distinctive characteristics compared to existing methods. We evaluate the proposed methodology across several benchmark datasets and demonstrate that it outperforms existing methods in accurately inferring interaction types. Overall the proposed model is data-efficient and generalizable to large systems when trained on smaller ones.
  arXiv  Detail & Related papers  (2023-04-30T19:45:04Z)
• Relational Local Explanations [11.679389861042]
  We develop a novel model-agnostic and permutation-based feature attribution algorithm based on relational analysis between input variables. We are able to gain a broader insight into machine learning model decisions and data.
  arXiv  Detail & Related papers  (2022-12-23T14:46:23Z)
• Co-Located Human-Human Interaction Analysis using Nonverbal Cues: A Survey [71.43956423427397]
  We aim to identify the nonverbal cues and computational methodologies resulting in effective performance. This survey differs from its counterparts by involving the widest spectrum of social phenomena and interaction settings. Some major observations are: the most often used nonverbal cue, computational method, interaction environment, and sensing approach are speaking activity, support vector machines, and meetings composed of 3-4 persons equipped with microphones and cameras, respectively.
  arXiv  Detail & Related papers  (2022-07-20T13:37:57Z)
• Information Interaction Profile of Choice Adoption [2.9972063833424216]
  We introduce an efficient method to infer the entities interaction network and its evolution according to the temporal distance separating interacting entities. The interaction profile allows characterizing the mechanisms of the interaction processes. We show that the effect of a combination of exposures on a user is more than the sum of each exposure's independent effect--there is an interaction.
  arXiv  Detail & Related papers  (2021-04-28T10:42:25Z)
• Towards Interaction Detection Using Topological Analysis on Neural Networks [55.74562391439507]
  In neural networks, any interacting features must follow a strongly weighted connection to common hidden units. We propose a new measure for quantifying interaction strength, based upon the well-received theory of persistent homology. A Persistence Interaction detection(PID) algorithm is developed to efficiently detect interactions.
  arXiv  Detail & Related papers  (2020-10-25T02:15:24Z)
• How does this interaction affect me? Interpretable attribution for feature interactions [19.979889568380464]
  We propose an interaction attribution and detection framework called Archipelago. Our experiments on standard annotation labels indicate our approach provides significantly more interpretable explanations than comparable methods. We also provide accompanying visualizations of our approach that give new insights into deep neural networks.
  arXiv  Detail & Related papers  (2020-06-19T05:14:24Z)

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.