R语言用逻辑回归、决策树和随机森林对信贷数据集进行分类预测

credit=read.csv("credit.csv", header = TRUE, sep = ",")

看起来所有变量都是数字变量，但实际上，大多数都是因子变量，

> str(credit)
'data.frame':  1000 obs. of  21 variables:
$ Creditability   : int  1 1 1 1 1 1 1 1 1 1 ...
$ Account.Balance : int  1 1 2 1 1 1 1 1 4 2 ...
$ Duration        : int  18 9 12 12 12 10 8  ...
$ Purpose         : int  2 0 9 0 0 0 0 0 3 3 ...

让我们将分类变量转换为因子变量，

> F=c(1,2,4,5,7,8,9,10,11,12,13,15,16,17,18,19,20)
> for(i in F) credit[,i]=as.factor(credit[,i])

现在让我们创建比例为1：2 的训练和测试数据集

> i_test=sample(1:nrow(credit),size=333)
> i_calibration=(1:nrow(credit))[-i_test]

我们可以拟合的第一个模型是对选定协变量的逻辑回归

> LogisticModel <- glm(Creditability ~ Account.Balance + Payment.Status.of.Previous.Credit + Purpose +
Length.of.current.employment +
Sex...Marital.Status, family=binomia

基于该模型，可以绘制ROC曲线并计算AUC（在新的验证数据集上）

> AUCLog1=performance(pred, measure = "auc")@y.values[[1]]
> cat("AUC: ",AUCLog1,"\n")
AUC:  0.7340997

glm(Creditability ~ .,
+  family=binomial,
+  data = credit[i_calibrat

> perf <- performance(pred, "tpr", "fpr
> AUCLog2=performance(pred, measure = "auc")@y.values[[1]]
> cat("AUC: ",AUCLog2,"\n")
AUC:  0.7609792

> prp(ArbreModel,type=2,extra=1)

(pred, "tpr", "fpr")
> plot(perf)


> cat("AUC: ",AUCArbre,"\n")
AUC:  0.7100323

> library(randomForest)
> RF <- randomForest(Creditability ~ .,
+ data = credit[i_calibration,])
> fitForet <- predict(RF,
> cat("AUC: ",AUCRF,"\n")
AUC:  0.7682367

> AUCfun=function(i){
+   set.seed(i)
+   i_test=sample(1:nrow(credit),size=333)
+   i_calibration=(1:nrow(credit))[-i_test]




+   summary(LogisticModel)
+   fitLog <- predict(LogisticModel,type="response",
+                     newdata=credit[i_test,])
+   library(ROCR)
+   pred = prediction( fitLog, credit$Creditability[i_test])
+   RF <- randomForest(Creditability ~ .,
+   data = credit[i_calibration,])
+   pred = prediction( fitForet, credit$Creditability[i_test])
+   return(c(AUCLog2,AUCRF))
+ }
> plot(t(A))