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Bootstrap与变权重相结合的多模型综合预测方法.docx

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Bootstrap与变权重相结合的多模型综合预测方法 Title:BootstrapandWeightedEnsemblePredictionMethodinCombinationwithMultipleModels Abstract: Theuseofmultiplemodelsinpredictiveanalyticshasshownsignificantpotentialtoimprovetheaccuracyandrobustnessofpredictions.ThispaperaimstoexploretheapplicationoftheBootstrapresamplingtechniqueandweightedensemblepredictionmethodincombinationwithmultiplemodelstofurtherenhancepredictionperformance.TheBootstrapmethodisusedtocreatemultiplebootstrapsamplesfromtheoriginaldataset,andeachsampleisthenusedtotrainadifferentpredictionmodel.Theweightedensemblepredictionmethodassignsdifferentweightstoeachindividualmodel'spredictionsbasedontheirperformanceonvalidationdata.Thefinalpredictionisobtainedbyaggregatingthepredictionsfromallmodels,weightedbytheirrespectiveweights.Experimentalresultsdemonstratethattheproposedapproachoutperformsindividualmodelsandtraditionalensemblemethodsintermsofpredictionaccuracyandrobustness. 1.Introduction: Predictiveanalyticsplaysavitalroleindiversefields,suchasfinance,healthcare,andmarketing,whereaccuratepredictionscanresultinmoreinformeddecision-making.However,individualmodelsoftensufferfromlimitationsincapturingthecomplexityandheterogeneityofreal-worlddata.Combiningmultiplemodelscanhelpovercometheselimitationsandimprovepredictionperformance.ThispaperinvestigatesthecombinationoftheBootstrapresamplingtechniqueandweightedensemblepredictionmethodtoexploittheadvantagesofmultiplepredictivemodels. 2.BootstrapResampling: TheBootstrapmethodinvolvesrandomlyresamplingtheoriginaldatasetwithreplacementtocreatemultiplebootstrapsamples.Eachbootstrapsampleisthenusedtotrainaseparatemodel.ByusingtheBootstrapmethod,weobtainadiversesetofmodels,eachtrainedonslightlydifferentsubsetsofthedata.Thisdiversityhelpstomitigatemodelbiasandimprovegeneralization. 3.WeightedEnsemblePrediction: OncemultiplemodelshavebeentrainedusingtheBootstraptechnique,weneedamethodtocombinetheirpredictionseffectively.Theweightedensemblepredictionapproachassignsdifferentweightstoeachmod