原文地址：http://xccds.github.io/2011/09/caret_9105.html/

模型建立好後，我們可以利用predict函式進行預測，例如預測檢測樣本的前五個

predict(gbmFit1, newdata = testx)[1:5]

gbmFit2= train(trainx, trainy,method = "treebag",trControl = fitControl)
models = list(gbmFit1, gbmFit2)

predValues = extractPrediction(models,testX = testx, testY = testy)
head(predValues)

     obs     pred model dataType  object
1 Active   Active   gbm Training Object1
2 Active   Active   gbm Training Object1
3 Active Inactive   gbm Training Object1
4 Active   Active   gbm Training Object1
5 Active   Active   gbm Training Object1 
 

testValues = subset(predValues, dataType == "Test")

probValues = extractProb(models,testX = testx, testY = testy)
testProbs = subset(probValues, dataType == "Test")

Pred1 = subset(testValues, model == "gbm")
Pred2 = subset(testValues, model == "treebag")
confusionMatrix(Pred1p

red,Pred1obs)
confusionMatrix(Pred2pred,Pred2obs)

          Reference
Prediction Active Inactive
  Active       65       12
  Inactive      9       45

               Accuracy : 0.8397          

Prediction Active Inactive
  Active       63       12
  Inactive     11       45

               Accuracy : 0.8244          

prob1 = subset(testProbs, model == "gbm")
prob2 = subset(testProbs, model == "treebag")
library(ROCR)
prob1lable=ifelse(prob1obs=='Active',yes=1,0)
pred1 = prediction(prob1Active,prob1lable)
perf1 = performance(pred1, measure="tpr", x.measure="fpr" )
plot( perf1 )