A Visual Tool for Interactive Model Explanation using Sensitivity Analysis

• URL: http://arxiv.org/abs/2508.04269v1
• Date: Wed, 06 Aug 2025 09:53:31 GMT
• Title: A Visual Tool for Interactive Model Explanation using Sensitivity Analysis
• Authors: Manuela Schuler,
• Abstract summary: We present SAInT, a Python-based tool for exploring and understanding the behavior of Machine Learning (ML) models.<n>Our system supports Human-in-the-Loop attribution (HITL) by enabling users to configure, train, evaluate, and explain models.<n>We demonstrate the system on a classification task predicting survival on the Titanic dataset and show how sensitivity information can guide feature selection and data refinement.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present SAInT, a Python-based tool for visually exploring and understanding the behavior of Machine Learning (ML) models through integrated local and global sensitivity analysis. Our system supports Human-in-the-Loop (HITL) workflows by enabling users - both AI researchers and domain experts - to configure, train, evaluate, and explain models through an interactive graphical interface without programming. The tool automates model training and selection, provides global feature attribution using variance-based sensitivity analysis, and offers per-instance explanation via LIME and SHAP. We demonstrate the system on a classification task predicting survival on the Titanic dataset and show how sensitivity information can guide feature selection and data refinement.

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