Oluwabunmi O. A., Oluwaferanmi I. R., Abdullai B. A.
{"title":"使用逻辑回归、决策树和随机森林的假新闻检测系统","authors":"Oluwabunmi O. A., Oluwaferanmi I. R., Abdullai B. A.","doi":"10.52589/bjcnit-ioyrpy7g","DOIUrl":null,"url":null,"abstract":"The purpose of this study is to design a fake news detection system with these three machine learning models, namely: Decision Tree, Random Forest, and Logistic Regression. These three different models were analysed to determine the most efficient model for accurately detecting fake news. The result obtained showcased Logistic Regression with an accuracy of 98.80%, Decision Tree with an accuracy of 99.64% and Random Forest with an accuracy of 99.23%. It is evident as deduction from the comparative analysis that our best model came out to be Decision Tree with an accuracy of 99.64%.","PeriodicalId":326452,"journal":{"name":"British Journal of Computer, Networking and Information Technology","volume":"11 24","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Fake News Detection System Using Logistic Regression, Decision Tree and Random Forest\",\"authors\":\"Oluwabunmi O. A., Oluwaferanmi I. R., Abdullai B. A.\",\"doi\":\"10.52589/bjcnit-ioyrpy7g\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The purpose of this study is to design a fake news detection system with these three machine learning models, namely: Decision Tree, Random Forest, and Logistic Regression. These three different models were analysed to determine the most efficient model for accurately detecting fake news. The result obtained showcased Logistic Regression with an accuracy of 98.80%, Decision Tree with an accuracy of 99.64% and Random Forest with an accuracy of 99.23%. It is evident as deduction from the comparative analysis that our best model came out to be Decision Tree with an accuracy of 99.64%.\",\"PeriodicalId\":326452,\"journal\":{\"name\":\"British Journal of Computer, Networking and Information Technology\",\"volume\":\"11 24\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-05-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"British Journal of Computer, Networking and Information Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.52589/bjcnit-ioyrpy7g\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"British Journal of Computer, Networking and Information Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.52589/bjcnit-ioyrpy7g","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fake News Detection System Using Logistic Regression, Decision Tree and Random Forest
The purpose of this study is to design a fake news detection system with these three machine learning models, namely: Decision Tree, Random Forest, and Logistic Regression. These three different models were analysed to determine the most efficient model for accurately detecting fake news. The result obtained showcased Logistic Regression with an accuracy of 98.80%, Decision Tree with an accuracy of 99.64% and Random Forest with an accuracy of 99.23%. It is evident as deduction from the comparative analysis that our best model came out to be Decision Tree with an accuracy of 99.64%.
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