Supplementary Data for "Visualizing the Explainability and Robustness of Machine Learning Models through Fitting Graphs"

This repository contains the supplementary materials accompanying the article“Visualizing the Explainability and Robustness of Machine Learning Models through Fitting Graphs”, submitted to Applied Artificial Intelligence. The datasets demonstrate the use of fitting graphs, a novel visualization technique designed to help model developers evaluate and interpret machine learning models in terms of explainability and robustness. The materials include: Real-world datasets (e.g., Baseball, Boston Housing, and Parkinson’s datasets) The data used to generate all the original figures in the article These resources support transparency and reproducibility for the analyses presented in the paper.本仓库包含投稿至《Applied Artificial Intelligence》（应用人工智能）期刊的论文《通过拟合图（Fitting Graphs）可视化机器学习模型的可解释性与鲁棒性》的配套补充材料。 本数据集展示了拟合图（Fitting Graphs）的应用场景：该技术为一种全新的可视化手段，旨在帮助模型开发者从可解释性与鲁棒性维度评估、解读机器学习模型。 本次补充材料包含以下内容： 真实世界数据集（例如棒球数据集、波士顿房价数据集与帕金森病数据集） 用于生成论文中所有原始图表的原始数据 本系列资源可为论文中呈现的分析工作提供透明度保障与可复现性支撑。