首页|Ministry of Agriculture and Rural Affairs Reports Findings in Machine Learning ( Explainable machine learning for predicting the geographical origin of Chinese O ysters via mineral elements analysis)
Ministry of Agriculture and Rural Affairs Reports Findings in Machine Learning ( Explainable machine learning for predicting the geographical origin of Chinese O ysters via mineral elements analysis)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting originating in Qingdao, Peopl e’s Republic of China, by NewsRx journalists, research stated, “The traceability of geographic origin is essential for guaranteeing the quality, safety, and pro tection of oyster brands. However, the current outcomes of traceability lack cre dibility as they do not adequately explain the model’s predictions.” The news reporters obtained a quote from the research from the Ministry of Agric ulture and Rural Affairs, “Consequently, we conducted a study to evaluate the ef ficacy of utilizing explainable machine learning combined with mineral elements analysis. The study findings revealed that 18 elements have the ability to deter mine regional orientation. Simultaneously, individuals should pay closer attenti on to the potential risks associated with oyster consumption due to the regional differences in essential and toxic elements they contain. Light gradient boosti ng machine (LightGBM) model exhibited indistinguishable performance, achieving f lawless accuracy, precision, recall, F1 score and AUC, with values of 96.77% , 96.43%, 98.53%, 97.32% and 0.998, resp ectively. The SHapley Additive exPlanations (SHAP) method was used to evaluate t he output of the LightGBM model, revealing differences in feature interactions a mong oysters from different provinces. Specifically, the features Na, Zn, V, Mg, and K were found to have a significant impact on the predictive process of the model.”
QingdaoPeople’s Republic of ChinaAsi aCyborgsEmerging TechnologiesMachine Learning
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