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A Visual Tool for Interactive Model Explanation using Sensitivity Analysis
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本文介绍SAInT,一款基于Python的可视化探索和理解的机器学习模型行为工具,支持用户通过交互式图形界面进行模型配置、训练、评估和解释,无需编程,自动化模型训练和选择,提供全局特征归因和实例解释。

arXiv:2508.04269v1 Announce Type: cross 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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机器学习 模型可视化 SAInT工具
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