ModelEWS:展示了将基于机器学习的辍学预警系统集成到数据仓库工作流中的示例的存储库-源码

上传者: 42109925 | 上传时间: 2021-07-18 17:03:09 | 文件大小: 83KB | 文件类型: ZIP
R
威斯康星辍学预警系统 欢迎使用威斯康星辍学预警系统 (DEWS)。 DEWS 是基于 R 统计计算语言的 DPI 数据仓库构建的机器学习应用程序。 DEWS 旨在成为一个灵活的半自动化机器学习系统,它评估数十种可能的机器学习算法以预测辍学,并选择性能最高的模型来为当前学生分配风险评分。 DEWS 背后的许多细节已经在其他地方进行了讨论,包括在 DPI 网站上: : 。 Knowles 2015 中讨论了机器学习方法的技术细节。 本文档用于描述 DEWS 程序本身。 DEWS 在设计上是一个模块化应用程序,允许它灵活地适应 DPI 的数据变化、新措施的可用以及新的机器学习技术的开发。 这种模块化由四个主要子程序组成(Knowles 2015)。 准备环境 数据采集 转换数据 火车模型 评分案例 每个子程序中都有许多步骤。 在大多数情况下,DEWS 包含一个自定义 R 函数来应用这些步

文件下载

资源详情

[{"title":"( 70 个子文件 83KB ) ModelEWS:展示了将基于机器学习的辍学预警系统集成到数据仓库工作流中的示例的存储库-源码","children":[{"title":"ModelEWS-master","children":[{"title":"man","children":[{"title":"buildTrainingPool.Rd <span style='color:#111;'> 1.12KB </span>","children":null,"spread":false},{"title":"preProcess.DEWSList.Rd <span style='color:#111;'> 748B </span>","children":null,"spread":false},{"title":"DEWScontrol.Rd <span style='color:#111;'> 1.78KB </span>","children":null,"spread":false},{"title":"cleanTrainingPool.Rd <span style='color:#111;'> 500B </span>","children":null,"spread":false},{"title":"assembleCohort.Rd <span style='color:#111;'> 769B </span>","children":null,"spread":false},{"title":"DEWS_search.Rd <span style='color:#111;'> 634B </span>","children":null,"spread":false},{"title":"unPreProc.Rd <span style='color:#111;'> 660B </span>","children":null,"spread":false},{"title":"lds_connect.Rd <span style='color:#111;'> 394B </span>","children":null,"spread":false},{"title":"dewsBootstrap.Rd <span style='color:#111;'> 669B </span>","children":null,"spread":false},{"title":"pullMobility.Rd <span style='color:#111;'> 730B </span>","children":null,"spread":false},{"title":"gatherData.pred.Rd <span style='color:#111;'> 1.08KB </span>","children":null,"spread":false},{"title":"pullGrad.Rd <span style='color:#111;'> 854B </span>","children":null,"spread":false},{"title":"gatherData.Rd <span style='color:#111;'> 1.10KB </span>","children":null,"spread":false},{"title":"modelSearch.Rd <span style='color:#111;'> 704B </span>","children":null,"spread":false},{"title":"constructModels.Rd <span style='color:#111;'> 1.25KB </span>","children":null,"spread":false},{"title":"findCohortYears.Rd <span style='color:#111;'> 685B </span>","children":null,"spread":false},{"title":"smote.Rd <span style='color:#111;'> 604B </span>","children":null,"spread":false},{"title":"exportPreds.Rd <span style='color:#111;'> 1002B </span>","children":null,"spread":false},{"title":"findBinary.Rd <span style='color:#111;'> 532B </span>","children":null,"spread":false},{"title":"pullCohort.Rd <span style='color:#111;'> 1.08KB </span>","children":null,"spread":false},{"title":"pullWSAS.Rd <span style='color:#111;'> 1000B </span>","children":null,"spread":false},{"title":"cleanUp.Rd <span style='color:#111;'> 315B </span>","children":null,"spread":false},{"title":"pullDiscipline.Rd <span style='color:#111;'> 755B </span>","children":null,"spread":false},{"title":"getCandidateMethods.Rd <span style='color:#111;'> 794B </span>","children":null,"spread":false},{"title":"checkCreds.Rd <span style='color:#111;'> 779B </span>","children":null,"spread":false},{"title":"makeCreds.Rd <span style='color:#111;'> 1.24KB </span>","children":null,"spread":false},{"title":"pullWSN.Rd <span style='color:#111;'> 481B </span>","children":null,"spread":false},{"title":"uncenter.Rd <span style='color:#111;'> 543B </span>","children":null,"spread":false},{"title":"cleanDV.DEWSList.Rd <span style='color:#111;'> 693B </span>","children":null,"spread":false},{"title":"findBestThresh.Rd <span style='color:#111;'> 384B </span>","children":null,"spread":false},{"title":"preProcess.pred.Rd <span style='color:#111;'> 641B </span>","children":null,"spread":false},{"title":"rawPreds.Rd <span style='color:#111;'> 484B </span>","children":null,"spread":false},{"title":"peerStats.Rd <span style='color:#111;'> 749B </span>","children":null,"spread":false},{"title":"scaleCenter.Rd <span style='color:#111;'> 773B </span>","children":null,"spread":false},{"title":"pullDemog.Rd <span style='color:#111;'> 1.19KB </span>","children":null,"spread":false},{"title":"modelSearchResults.Rd <span style='color:#111;'> 685B </span>","children":null,"spread":false},{"title":"expectedGrad.Rd <span style='color:#111;'> 795B </span>","children":null,"spread":false},{"title":"findKeys.Rd <span style='color:#111;'> 374B </span>","children":null,"spread":false},{"title":"makeDEWSList.Rd <span style='color:#111;'> 1.29KB </span>","children":null,"spread":false},{"title":"postProcess.pred.Rd <span style='color:#111;'> 539B </span>","children":null,"spread":false},{"title":"installPackages.Rd <span style='color:#111;'> 473B </span>","children":null,"spread":false}],"spread":false},{"title":".gitignore <span style='color:#111;'> 132B </span>","children":null,"spread":false},{"title":"README.md <span style='color:#111;'> 14.58KB </span>","children":null,"spread":false},{"title":".Rbuildignore <span style='color:#111;'> 64B </span>","children":null,"spread":false},{"title":"DEWS.R <span style='color:#111;'> 3.30KB </span>","children":null,"spread":false},{"title":"packrat","children":[{"title":"init.R <span style='color:#111;'> 7.01KB </span>","children":null,"spread":false},{"title":"packrat.lock <span style='color:#111;'> 10.54KB </span>","children":null,"spread":false},{"title":"packrat.opts <span style='color:#111;'> 207B </span>","children":null,"spread":false}],"spread":true},{"title":"tests","children":[{"title":"QA_exportFile.R <span style='color:#111;'> 9.35KB </span>","children":null,"spread":false}],"spread":true},{"title":".Rprofile <span style='color:#111;'> 115B </span>","children":null,"spread":false},{"title":"LICENSE <span style='color:#111;'> 17.62KB </span>","children":null,"spread":false},{"title":"DESCRIPTION <span style='color:#111;'> 441B </span>","children":null,"spread":false},{"title":"inst","children":[{"title":"old","children":[{"title":"prediction_module_bayesian.R <span style='color:#111;'> 2.52KB </span>","children":null,"spread":false},{"title":"robustness.R <span style='color:#111;'> 2.83KB </span>","children":null,"spread":false},{"title":"setup_Bayes.R <span style='color:#111;'> 1.43KB </span>","children":null,"spread":false}],"spread":true},{"title":"doc","children":[{"title":"exportspecforEDVANTAGE.txt <span style='color:#111;'> 1.15KB </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"R","children":[{"title":"dataTransform.R <span style='color:#111;'> 16.47KB </span>","children":null,"spread":false},{"title":"constructModels.R <span style='color:#111;'> 6.26KB </span>","children":null,"spread":false},{"title":"dataExtract.R <span style='color:#111;'> 21.67KB </span>","children":null,"spread":false},{"title":"modelReport.R <span style='color:#111;'> 1.69KB </span>","children":null,"spread":false},{"title":"constructImputationModels.R <span style='color:#111;'> 10.84KB </span>","children":null,"spread":false},{"title":"predictCaret.R <span style='color:#111;'> 3.99KB </span>","children":null,"spread":false},{"title":"imputation_routines.R <span style='color:#111;'> 5.32KB </span>","children":null,"spread":false},{"title":"lds_connect.R <span style='color:#111;'> 5.30KB </span>","children":null,"spread":false},{"title":"pkgs.R <span style='color:#111;'> 2.58KB </span>","children":null,"spread":false},{"title":"imputeCaretSearch.R <span style='color:#111;'> 6.69KB </span>","children":null,"spread":false},{"title":"exportPrep.R <span style='color:#111;'> 9.50KB </span>","children":null,"spread":false},{"title":"modelsearchCaret.R <span style='color:#111;'> 8.46KB </span>","children":null,"spread":false}],"spread":false},{"title":"modelErrors.txt <span style='color:#111;'> 802B </span>","children":null,"spread":false},{"title":"NAMESPACE <span style='color:#111;'> 1.09KB </span>","children":null,"spread":false}],"spread":false}],"spread":true}]

评论信息

免责申明

【只为小站】的资源来自网友分享,仅供学习研究,请务必在下载后24小时内给予删除,不得用于其他任何用途,否则后果自负。基于互联网的特殊性,【只为小站】 无法对用户传输的作品、信息、内容的权属或合法性、合规性、真实性、科学性、完整权、有效性等进行实质审查;无论 【只为小站】 经营者是否已进行审查,用户均应自行承担因其传输的作品、信息、内容而可能或已经产生的侵权或权属纠纷等法律责任。
本站所有资源不代表本站的观点或立场,基于网友分享,根据中国法律《信息网络传播权保护条例》第二十二条之规定,若资源存在侵权或相关问题请联系本站客服人员,zhiweidada#qq.com,请把#换成@,本站将给予最大的支持与配合,做到及时反馈和处理。关于更多版权及免责申明参见 版权及免责申明