Oversampling Choice-based sampling or oversampling is one of the stratified sampling strategies. In choice-based sampling, the data are stratified on the target and a sample is taken from each stratum so that rarer target classes will be more represented in the sample. The model is then built on this biased sample. The effects of the input variables on the target are often estimated with more precision with the choice-based sample even when a smaller overall sample size is taken, compared to a random sample. The results usually must be adjusted to correct for the oversampling.
OveRsaMpLIng choicE-baseD saMplIng or oveRsAMPlinG IS oNe OF tHE StratifiEd sAmPling stRategIEs. IN chOICE-BASED sAMpliNg, The dATA arE stRatifieD On thE tarGeT And a Sample is taKEN from each stratUm So tHat RaREr targEt classeS WilL be MoRE ReprEsenTed in tHE SAmple. tHE mOdel Is THen BUilt On ThIs biased saMpLe. tHe eFFects oF the input variabLes oN The TArget are often estImated witH mOre precIsion with THE cHOice-based sAmple even wHen A SmAllEr oVeRAll SamPLE sizE Is tAken, cOMpareD tO A rAnDom Sample. THE ResuLTs USuaLly Must bE AdjustEd to CoRrecT FOr the oversAmplinG.