摘要
一位新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-人工智能的新研究是一篇新报道的主题。根据NewsRx记者从康涅狄格州S Torrs发回的新闻报道，研究表明，“靠近电线的树木在风暴期间会对公用事业基础设施造成重大破坏，导致巨大的经济和社会成本。”新闻记者从科内蒂克特大学的研究中获得了一句话：“这项研究调查了非参数机器学习算法在更精细的空间尺度上模拟配电线路与树木相关的停电风险的有效性。我们使用了植被风险模型(VRM)，包括15个预测变量，这些预测变量来自路边树木数据、景观信息、植被管理记录、采用决策树(DT)、随机森林(RF)、k近邻Neigh bor(k-NN)、极值梯度boosting(XGBoost)和支持向量机(SVM)技术对VRM的性能进行了评估,结果表明,RF算法的准确率为0.753,AUC-ROC为0.746,精度为0.671.从总体性能来看，SVM的召回值最高为0.727.，RF是最好的机器学习算法，DT是最不合适的。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on artificial intelligenc e is the subject of a new report. According to news reporting originating from S torrs, Connecticut, by NewsRx correspondents, research stated, "Trees in proximi ty to power lines can cause significant damage to utility infrastructure during storms, leading to substantial economic and societal costs." The news correspondents obtained a quote from the research from University of Co nnecticut: "This study investigated the effectiveness of non-parametric machine learning algorithms in modeling tree-related outage risks to distribution power lines at a finer spatial scale. We used a vegetation risk model (VRM) comprising 15 predictor variables derived from roadside tree data, landscape information, vegetation management records, and utility infrastructure data. We evaluated the VRM's performance using decision tree (DT), random forest (RF), k-Nearest Neigh bor (k-NN), extreme gradient boosting (XGBoost), and support vector machine (SVM ) techniques. The RF algorithm demonstrated the highest performance with an accu racy of 0.753, an AUC-ROC of 0.746, precision of 0.671, and an F1-score of 0.693 . The SVM achieved the highest recall value of 0.727. Based on the overall perfo rmance, the RF emerged as the best machine learning algorithm, whereas the DT wa s the least suitable."
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