নমস্কার
70K+
Cardiovascular Records
0.83
Leakage-free ROC-AUC
2
Clustering Strategies
(Overview)
PulseisacardiovascularMLinvestigationwhereIexploredwhetherclusteringcouldimprovediseaseprediction,anddiscoveredthatamajorperformancegainwasactuallycausedbytarget leakage.Ithenvalidatedthefindingthroughcross-validationandexperimentedwithleakage-freeclusteringapproaches.
(The Challenge)
ThemainchallengewasunderstandingwhyaddingclusteringinformationcausedtheXGBoostmodelperformancetoincreasedramatically.Hyperparametertuningproducedonlyasmallimprovement,whileintroducingthetargetvariableintoclusteringresultedinamuchlargerjump.Thisraisedthequestionofwhethertheimprovementrepresentedgenuinepredictiveinformationorhiddentargetleakage.
(The Solution)
Icomparedmultiplebaselinemodels,selectedXGBoost,experimentedwithK-ModesandK-Prototypesclustering,andtracedtheunexpectedperformanceincreasebacktotargetleakage.Ithenseparatedtarget-inclusiveandtarget-excludedclustering,evaluatedbothusingcross-validation,andconfirmedthattheapparentimprovementwaslargelycausedbyleakage.Finally,Iexperimentedwithleakage-freeclusteringtoinvestigatewhetherclusteringcouldprovidegenuinepredictivevaluewithoutusingthetarget.
Performedexploratorydataanalysis,datacleaning,preprocessing,andfeatureanalysisonalargecardiovasculardataset.
ComparedmultiplebaselinemachinelearningmodelsandselectedXGBoostbasedonitsoverallclassificationperformance.
ExperimentedwithhyperparametertuningandclusteringusingK-Modestoinvestigatewhetherunsupervisedgroupingcouldimprovecardiovascularprediction.
InvestigatedanunexpectedperformanceincreaseafterincorporatingclusterinformationintotheXGBoostmodel,leadingtothediscoveryoftargetleakage.
Validatedtheleakagethroughcross-validationandcontrolledexperimentscomparingtarget-inclusiveandtarget-excludedclusteringapproaches.
ExperimentedwithK-Prototypesclusteringtodevelopaleakage-freeclusteringapproachandevaluatewhethertheperformanceimprovementcouldbeachievedwithoutusingthetargetvariable.



Built with
Haveaprojectinmind?
dattaiman56@gmail.com