View source: R/CSMES.predictPareto.R
CSMES.predictPareto | R Documentation |
This function generates predictions for all pareto-optimal ensemble classifier candidates as identified through the first training stage of CSMES (CSMES.ensSel
).
CSMES.predictPareto(ensSelModel, newdata)
ensSelModel |
ensemble selection model (output of |
newdata |
data.frame or matrix containing data to be scored |
An object of the class CSMES.predictPareto
which is a list with the following two components:
Pareto_predictions_c |
A vector with class predictions. |
Paret_predictions_p |
A vector with probability predictions. |
Koen W. De Bock, kdebock@audencia.com
De Bock, K.W., Lessmann, S. And Coussement, K., Cost-sensitive business failure prediction when misclassification costs are uncertain: A heterogeneous ensemble selection approach, European Journal of Operational Research (2020), doi: 10.1016/j.ejor.2020.01.052.
CSMES.ensSel
, CSMES.predict
, CSMES.ensNomCurve
##load data library(rpart) library(zoo) library(ROCR) library(mco) data(BFP) ##generate random order vector BFP_r<-BFP[sample(nrow(BFP),nrow(BFP)),] size<-nrow(BFP_r) ##size<-300 train<-BFP_r[1:floor(size/3),] val<-BFP_r[ceiling(size/3):floor(2*size/3),] test<-BFP_r[ceiling(2*size/3):size,] ##generate a list containing model specifications for 100 CART decisions trees varying in the cp ##and minsplit parameters, and trained on bootstrap samples (bagging) rpartSpecs<-list() for (i in 1:100){ data<-train[sample(1:ncol(train),size=ncol(train),replace=TRUE),] str<-paste("rpartSpecs$rpart",i,"=rpart(as.formula(Class~.),data,method=\"class\", control=rpart.control(minsplit=",round(runif(1, min = 1, max = 20)),",cp=",runif(1, min = 0.05, max = 0.4),"))",sep="") eval(parse(text=str)) } ##generate predictions for these models hillclimb<-mat.or.vec(nrow(val),100) for (i in 1:100){ str<-paste("hillclimb[,",i,"]=predict(rpartSpecs[[i]],newdata=val)[,2]",sep="") eval(parse(text=str)) } ##score the validation set used for ensemble selection, to be used for ensemble selection ESmodel<-CSMES.ensSel(hillclimb,val$Class,obj1="FNR",obj2="FPR",selType="selection", generations=10,popsize=12,plot=TRUE) ## Create Ensemble nomination curve enc<-CSMES.ensNomCurve(ESmodel,hillclimb,val$Class,curveType="costCurve",method="classPreds", plot=FALSE)
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