错误:调优参数网格应该有列mtry

Question

我试图在R中使用caret来训练一个随机森林模型，我想通过使用expand.grid函数来调优参数以获得最佳值。但是，我一直收到这样的错误：

Error: The tuning parameter grid should have columns mtry

这是我的密码。我在这里使用的数据称为scoresWithResponse

ctrlCV = trainControl(method = 'cv', number = 10 , classProbs = TRUE , savePredictions = TRUE, summaryFunction = twoClassSummary )

rfGRID = expand.grid(interaction.depth = c(2, 3, 5, 6, 7, 8, 10),
                     n.trees = c(50,75,100,125,150,200,250), 
                     shrinkage = seq(.005, .2,.005),
                     n.minobsinnode = c(5,7,10, 12 ,15, 20),
                    nodesize = c(1:10),
                    mtry = c(1:10))

RF_loop_trn = c()
RF_loop_tst = c()

for(i in (1:5)){
  print(i)
  
  IND = createDataPartition(y = scoresWithResponse$response, p=0.75, list = FALSE)
  scoresWithResponse.trn = scoresWithResponse[IND, ]
  scoresWithResponse.tst = scoresWithResponse[-IND,]
  
  rfFit = train(response~., data = scoresWithResponse.trn,
                importance = TRUE,
                method = "rf",
                metric="ROC",
                trControl = ctrlCV,
                tuneGrid = rfGRID,
                classProbs = TRUE,
                summaryFunction = twoClassSummary
  )
  
  
  RF_loop_trn[i] = auc(roc(scoresWithResponse.trn$response,predict(rfFit,scoresWithResponse.trn, type='prob')[,1]))
  RF_loop_tst[i] = ahaveroc(scoresWithResponse.tst$response,predict(rfFit,scoresWithResponse.tst, type='prob')[,1]))
  
}

在调查了一段时间后，有一些建议，例如从github重新下载插入符号包，在expand.grid中的每个参数之前添加一个expand.grid，只在mtry参数之前添加一个点(类似于.mtry)，将mtry添加到train函数中，而不是expand.grid。我试过所有这些，它们都会产生同样的错误。

我应该在哪里以及如何添加mtry参数？是什么导致了这个错误？

Answer 1

我还不能发表评论，所以我会回复

您是否尝试将mtry参数直接插入到train函数中？例如：

rfGRID = expand.grid(interaction.depth = c(2, 3, 5, 6, 7, 8, 10),
                     n.trees = c(50,75,100,125,150,200,250), 
                     shrinkage = seq(.005, .2,.005),
                     n.minobsinnode = c(5,7,10, 12 ,15, 20),
                    nodesize = c(1:10)
                    )


rfFit = train(response~., data = scoresWithResponse.trn,
                importance = TRUE,
                method = "rf",
                metric="ROC",
                trControl = ctrlCV,
                tuneGrid = rfGRID,
                classProbs = TRUE,
                summaryFunction = twoClassSummary,
                mtry = 1000

  )