文件名称：Facebook-V:Kaggle招聘竞赛获胜作品的清理代码
文件大小：65.64MB
文件格式：ZIP
更新时间：2024-05-15 15:33:14
R
Facebook V：预测签入 清理了获胜代码。 我上的文章详细介绍了该方法。 从“原始数据”到获奖作品的高级说明在“说明” pdf中有所提及。 请随时讨论中奖主题中可能不清楚的任何内容： : 。 还请确保检查我的探索性分析Shiny应用程序： :
【文件预览】：
Facebook-V-master
----Exploratory analysis()
--------timeHoursDay.R(10KB)
--------extremeDailyPopularityAnalysis.R(3KB)
--------Items to research.pdf(865KB)
--------Items to research.docx(564KB)
--------itemsResearch.R(16KB)
--------trainSubset.R(983B)
--------accuracyAnalysis.R(4KB)
--------Shiny app - Facebook V()
----.gitignore(649B)
----README.md(743B)
----Data()
--------rawToRds.R(383B)
--------blockGenerator.R(2KB)
--------trainSubset.R(983B)
----Evaluate predictions()
--------evaluatePredictions.R(7KB)
----Submission()
--------04-06-2016()
--------Submission feedback.xlsx(24KB)
--------06-07-2016()
--------scrapingKG.R(2KB)
----Strategy()
--------Ideas.pdf(323KB)
--------Nomenclature of studied data sets.docx(14KB)
--------Workflow snapshot June 10, 2016.docx(27KB)
--------Workflow snapshot June 10, 2016.pdf(483KB)
--------Ideas.docx(3KB)
--------Nomenclature of studied data sets.pdf(227KB)
----First level learners()
--------xgboost()
--------nnH2O()
--------mainLearnerLogic.R(4KB)
--------combineModelPreds.R(6KB)
--------First level learners summary validation 1-4.png(9KB)
--------First level learners summary validation 21-30.png(10KB)
--------nnet()
--------manualFeatureImportanceRank.R(11KB)
--------Simple xgboost blend()
--------validateModelPreds.R(4KB)
--------Caret methods.url(124B)
----Feature engineering()
--------studyDensityVersusCount.R(920B)
--------validateFeatures.R(3KB)
--------weeklyDensityExtraction.R(2KB)
--------createRegionDensityFeatures.R(3KB)
--------validateFeatureRangesTrainTest.R(1KB)
--------weeklyDensityForecast.R(3KB)
--------combineNeighborSummaryFeatures.R(40KB)
--------createWeeklySummaryFeatures.R(1KB)
--------createAccuracySummaryFeatures.R(4KB)
--------createFeaturesSummary.R(29KB)
----Instructions.pdf(361KB)
----Instructions.docx(18KB)
----.gitattributes(378B)
----Downsampling()
--------downsamplerTimeRandomLocTest.R(3KB)
--------downsamplerTimeRandomLocTrain.R(9KB)
--------downsampleTimeRandomLocTrainCheck.R(1KB)
--------downsampleTimeRandomLocTrainCheck - Copy.R(585B)
----winningSubmission.7z(62.3MB)
----References()
--------Big data in R.url(144B)
----Second level learners()
--------xgboostValidationAvgBlend.R(5KB)
--------featureRanking.R(2KB)
--------Final model selection.docx(18KB)
--------Final model selection.pdf(367KB)
--------xgboost.R(14KB)
----Common()
--------apk.R(2KB)
----Candidate selection()
--------getTopKNNDT.R(7KB)
--------midKnnWrapper.R(3KB)
--------roundingBugAnalysis.R(1KB)
--------.Rhistory(0B)
--------fastOrdeR package()
--------candidateSel Tier I NN topCalc.R(4KB)
--------midKnnCalc.R(6KB)