SCIENCE&TECHNOLOGYINFORMATION201013、。:、、、。1,,。,(,、、)。,(,,:6,10)。,,,。,,。。,。,。2,,,,、,、。。。:。3,。、、。,,,2-2.5。,。,,。3.1,,,“”,“”。,,,“”、“”,“”,“”。,,;,,。,,,。6、1218。3.2,,,,;,,,,,,,,。,,。,(Lewise)[1]199215001500,。,、。、、,,。,,、50:50,50:50、。,,、,,,,。3.3。:、、、。,“”。,“”。。()。。。,,。4,“”“”,,“”“”。,。:90。5,。:5.1,,、,、、、、。5.2、。,;,;(510006)【】,。。,。,。【】;;○IT○464201013SCIENCE&TECHNOLOGYINFORMATION。:、、、、。。5.3,。“”、“”“”:●●●●WOE,,。:(1);(2)(Giniscore)(Informationvalue)。(3)WOE(WeightofEvidence),。WOE。WOE:WOEattribute=ln(Pattributenonevent/Pattributeevent)(1)Pattributeevent=nattibuteevent/Nevent(2)Pattributenonevent=nattibutenonevent/Nnonevent(3),Nevent,Nnonevent,nattibuteevent,nattibutenonevent。(WeightofEvidence),WOE,。,(Informationvalue)。WOE,。:Informationvalue=Σattribute[(Pattributenonevent-Pattributeevent)*WOEattribute](4)0.02。0.5,(over-predicting)。:◆,,m,1、2、…m,,1。◆,i,:Gini=1-2Σi=2m(nievent*Σ1i=1ninonevent)+Σim(nievent*ninonevent)(Nevent*Nnonevent)◆◆*100(5)0.1,20。,,。,,。6,,Logistic。,Logistic。Logistic,,。,,Logistic,“”/“”,。。Logistic,,01,,。Logistic,,:Logit(pi)=log(p_good/p_bad)=log(odds)=age_woe*bage+status_woe*bcar++a(6)Logit(pi),log(P(bad)/P(good)),P(bad)/P(good)0∞,log(P(bad)/P(good))-∞+∞。“”,,。(LogisticRegression)/(good/badodds),/(good/badodds)。,。:WOE,,,:(woei*βi+an)*factor+offsetn(7),:score=log(odds)*factor+offset=(ni=1Σ(woei*βi)+a)*factor+offset=(ni=1Σ(woei*βi+an))*factor+offset=ni=1Σ((woei*βi+an)*factor+offsetn)(8):●/=50/1600●20/:600=log(50)*factor+offset620=log(100)*factor+offsetfactor=20/log(2)offset=600-factor*log(50)7。,,。(80%),(20%)。(),;,。,、KS,。【】[1],..:,2006.[:]●(463),,,。。【】[1].Java[M].:,2007.[2].Java[J].,2009(33).[3].JAVA[J].,2007(2).[4],.[J].,2005(6).:(1987—),,,,,。[:]●●○IT○465