首页|University of Alberta Researcher Updates Knowledge of Support Vector Machines (U sing Explainable AI for Enhanced Understanding of Winter Road Safety: Insights w ith Support Vector Machines and SHAP)
University of Alberta Researcher Updates Knowledge of Support Vector Machines (U sing Explainable AI for Enhanced Understanding of Winter Road Safety: Insights w ith Support Vector Machines and SHAP)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on . According to news reporting originating from Edmonton, Canada, by NewsRx corresp ondents, research stated, "This study investigates the utility of machine learni ng (ML) in understanding and mitigating winter road risks." The news editors obtained a quote from the research from University of Alberta: "Despite their capability in managing complex data structures, ML models often l ack interpretability. We address this issue by integrating Shapley Additive Expl anations (SHAP) with a Support Vector Machine (SVM) model. Utilizing a comprehen sive dataset of 231 snowstorm events collected in the city of Edmonton across tw o winter seasons, the SVM model achieved an accuracy rate of 87.2%. Following model development, a SHAP summary plot was employed to identify the c ontribution of individual features to collision predictions-an insight not achie vable through ML alone."
University of AlbertaEdmontonCanadaNorth and Central AmericaEmerging TechnologiesMachine LearningSupport Vec tor MachinesVector Machines
NETL
NSTL
