caret包应用之四：模子预测与检讨

模子成立好后，我们可以操作predict函数举办预测，譬喻预测检测样本的前五个

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

为了较量差异的模子，还可用装袋决定树成立第二个模子，定名为gbmFit2

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

另一种获得预测功效的要领是利用extractPrediction函数，获得的部门功效如下显示

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 

6 Active Active gbm Training Object1

从中可提取检讨样本的预测功效

testValues = subset(predValues, dataType == “Test”)

假如要获得预测概率，则利用extractProb函数

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

对付分类问题的效能检讨，最重要的是调查预测功效的夹杂矩阵

Pred1 = subset(testValues, model == “gbm”)
Pred2 = subset(testValues, model == “treebag”)
confusionMatrix(Pred1$pred, Pred1$obs)
confusionMatrix(Pred2$pred, Pred2$obs)

功效如下，可见第一个模子在精确率要比第二个模子略好一些

          Reference

Prediction Active Inactive

  Active       65       12

  Inactive      9       45
               Accuracy : 0.8397          

Reference

Prediction Active Inactive

  Active       63       12

  Inactive     11       45
               Accuracy : 0.8244          

最后是操作ROCR包来绘制ROC图

prob1 = subset(testProbs, model == “gbm”)
prob2 = subset(testProbs, model == “treebag”)
library(ROCR)
prob1$lable=ifelse(prob1$obs==’Active’,yes=1,0)
pred1 = prediction(prob1$Active,prob1$lable)
perf1 = performance(pred1, measure=”tpr”, x.measure=”fpr” )
plot( perf1 )