Exploratory Explainable Machine Learning for Crop-Yield Interpretation: A Comparative Analysis of XGBoost, LightGBM, and CatBoost
比较XGBoost、LightGBM和CatBoost在作物产量可解释建模中的表现，强调评估协议与数
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#XGBoost SHAP
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没有这个标签，内容接近Unraveling nonlinear ecosystem water use efficiency dynamics and environmental drivers across Iran using remote sensing and XGBoost-SHAP
用遥感与XGBoost-SHAP解析伊朗2001-2025年生态系统水分利用效率的非线性动态及驱动因
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Design and Implementation of an Ensemble Learning Based Decision Support Model for Crop Selection in Precision Agriculture
提出SCR-XGB五层框架，用梯度提升树集成从土壤和气候参数推荐作物。
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Image-Based Soil Nutrient Prediction Using Machine Learning and Deep Learning Models
用土壤图像结合机器学习与深度学习模型预测氮磷钾含量。
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Spatial and Temporal characteristics and driving force analysis of vegetation cover change in Shanxi Province, China based on kNDVI and XGBoost-SHAP model
基于kNDVI与XGBoost-SHAP分析山西2000-2024年植被覆盖时空变化及驱动机制。
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Portable Hyperspectral Imaging Combined with Interpretable PLS-DA-SHAP for Geographical Origin Traceability and Authenticity Discrimination of Poria cocos (Fushen)
本文提出便携式高光谱成像结合可解释PLS-DA-SHAP方法,用于茯苓(Fushen,Poria cocos)的地理来源溯源与真实性鉴别。研究展示了如何将机器学习与SHAP可解释性相结合,应用于中药材产地溯源。原文标题:Portable hyperspectral imaging combined with interpretable PLS-DA-SHAP for geographical origin traceability and authenticity discrimination of Poria cocos (Schw.) Wolf (Fushen),作者:Xing W、Zhang Y、Wei X、Nie S、Cheng X、Cai M、Yang Q、Wang Y,发表于Frontiers in Sustainable Food Systems 2026年第10卷doi:10.3389/fsufs.2026.1891249,2026年7月15日接收,8月7日发表。本研究为基于光谱成像的农产品/中药材产地溯源提供了可解释AI方法范式。
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Debris Flow Susceptibility Mapping Using Explainable Deep Learning in Eastern Hindukush Pakistan
用CNN结合SHAP绘制巴基斯坦东兴都库什泥石流易发性图并解释关键因子。
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Risk-based prioritization of official animal welfare inspections using machine learning
用机器学习基于常规行政与兽医数据，对奥地利农场动物福利违规风险进行优先级排序。
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