caret包应用之三：建模与参数优化

在举办建模时，需对模子的参数举办优化，在caret包中其主要函数呼吁是train。

首先获得颠末特征选择后的样本数据，并分别为练习样本和检讨样本

newdata4=newdata3[,Profile$optVariables]
inTrain = createDataPartition(mdrrClass, p = 3/4, list = FALSE)
trainx = newdata4[inTrain,]
testx = newdata4[-inTrain,]
trainy = mdrrClass[inTrain]
testy = mdrrClass[-inTrain]

然后界说模子练习参数，method确定多次交错检讨的抽样要领，number确定了分另外重数， repeats确定了重复次数。

fitControl = trainControl(method = “repeatedcv”, number = 10, repeats = 3,returnResamp = “all”)

确定参数选择范畴，本例建模筹备利用gbm算法，相应的参数有如下三项

gbmGrid = expand.grid(.interaction.depth = c(1, 3),.n.trees = c(50, 100, 150, 200, 250, 300),.shrinkage = 0.1)

操作train函数举办练习，利用的建模要领为晋升决定树要领，

gbmFit1 = train(trainx,trainy,method = “gbm”,trControl = fitControl,tuneGrid = gbmGrid,verbose = FALSE)

从功效可以调查到interaction.depth取1，n.trees取150时精度较高

interaction.depth  n.trees  Accuracy  Kappa  Accuracy SD  Kappa SD

  1                  50       0.822     0.635  0.0577       0.118

  1                  100      0.824     0.639  0.0574       0.118

  1                  150      0.826     0.643  0.0635       0.131

  1                  200      0.824     0.64   0.0605       0.123

  1                  250      0.816     0.623  0.0608       0.124

  1                  300      0.824     0.64   0.0584       0.119

  3                  50       0.816     0.621  0.0569       0.117

  3                  100      0.82      0.631  0.0578       0.117

  3                  150      0.815     0.621  0.0582       0.117

  3                  200      0.82      0.63   0.0618       0.125

  3                  250      0.813     0.617  0.0632       0.127

  3                  300      0.812     0.615  0.0622       0.126   

同样的图形调查

plot(gbmFit1)