摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-机器学习的最新数据-支持RT向量机在一份新的报告中呈现。根据NewsRx记者从印度海得拉巴发回的新闻报道，研究表明，“雷暴是影响人、动物和经济的自然灾害。通过提前发现雷暴的发生，可以避免雷暴的有害影响。”我们的新闻记者从计算机科学与工程系的研究中获得了一句话，“在这方面，目前的工作使用软计算技术，如k-近邻(KNN)、决策树(DT)、逻辑回归(LR)和支持向量机(SVM)，以及各种核函数，来对兰奇地区雷暴的发生进行分类，”印度。这些技术人员使用两个数据集进行训练和测试：每日平均和每小时平均气象数据集。本研究的主要目的是找出哪种数据基T-分类器组合最适合对兰奇雷暴发生进行分类。没有发现分类器能充分地对日平均Da Taset或修改后的日平均数据集进行分类。采用不同分类器得到的雷暴事件发生率的F值比较了不同分类器得到的雷暴事件发生率的F值,结果表明:采用径向基函数支持向量机得到的雷暴事件发生率最高,小时数据集得到的雷暴事件发生率最高,时间数据集得到的雷暴事件发生率最高。CL分类。对于雷暴类的总体和唯一发生率，SVM-R BF分别得到0.81和0.74的F分。其他方法，如网格搜索和Bagging，已经被用来提高SVM-RBF的性能。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on Machine Learning - Suppo rt Vector Machines are presented in a new report. According to news originating from Hyderabad, India, by NewsRx correspondents, research stated, “Thunderstorms are natural disasters that impact people, animals, and the economy. Thunderstor ms’ detrimental repercussions can be avoided by identifying their occurrence in advance.” Our news journalists obtained a quote from the research from the Department of C omputer Sciences and Engineering, “The current work, in this respect, uses soft computing techniques such as K-Nearest Neighbour (KNN), Decision Tree (DT), Logi stic Regression (LR), and Support Vector Machine (SVM) with various kernel funct ions to categorize the occurrence of thunderstorms over Ranchi, India. These tec hniques were trained and tested using two data sets: daily average and hourly me teorological datasets. The primary purpose of this study is to find which datase t-classifier combination is optimal for categorizing thunderstorm occurrence in Ranchi. No classifier was found to adequately classify either the Day Average Da taset or the Modified Day Average Dataset. On the other hand, the Hourly Dataset was found to be more balanced in terms of the number of thunderstorms that occu rred than the Day Average and Modified Average datasets. The F-Score value of th e incidence of thunderstorm incidents after using different classifiers was used to compare the outcomes of these datasets. The results reveal that using SVM wi th radial basis function. The Hourly Dataset is the best for thunderstorm day cl assification. For the overall and only incidence of thunderstorms classes, SVM-R BF gets 0.81 and 0.74 F-Scores, respectively. Other approaches, like grid search and Bagging, have been used to increase SVM-RBF performance.”
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