[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2400":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"view_count":31,"doi":8,"paper":32,"created_at":41},2400,"Do Agricultural Subsidy Profiles Signal Bank-Authorized Lending? A Two-Stage Interpretable Machine Learning Analysis of Credit Access and Allocation","https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F18\u002F9321","Sustainability 2026, 18(18):9321（2026-09-10）。北京市农林科学院数据科学与农业经济研究所胡延祥等基于北京2970家农业企业及合作社行政记录，分析多源补贴档案与银行授权信贷的关联。两阶段梯度提升树模型结合SHAP特征归因：在剩余实体类型中，组合模型预测精度ROC-AUC达0.97。原始行政特征对条件性授权信贷金额预测价值有限，纳入供应分数可显著提升。",null,"MDPI Sustainability | 2026-09-10","2026-09-10T00:00:00Z","论文",10,false,78,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},16,22,18,14,8,1,"基于2970家农业经营主体行政记录的两阶段可解释机器学习研究，方法新颖、数据扎实，对补贴档案与银行授权信贷的关联提供了实证证据，值得进入每日精选。",[24],{"name":9,"url":6},[26,27,28,29,30],"可解释机器学习","农村金融","农业补贴","农业数据","农业信贷",0,{"doi":8,"openalex_id":8,"authors":33,"venue":8,"cited_by_count":31,"oa_url":8,"card":34,"direction":38,"ingested_from":40},[],{"tldr":35,"method":36,"finding":37,"direction":38,"opportunity":39},"基于北京2970家农业主体行政记录，用两阶段可解释机器学习分析补贴档案与银行授权信贷的关联。","两阶段梯度提升树结合SHAP归因，使用北京农业企业及合作社行政记录数据。","组合模型预测授权信贷精度ROC-AUC达0.97，纳入供应分数可显著提升金额预测。","数字乡村与农业信息化","可探索补贴数据跨部门共享机制与可解释模型在涉农信贷风控中的落地路径。","agent","2026-09-14T00:06:33.352853Z"]