caretEnsmble與SVM（問題），不同的培訓數據集

Question

以下是一個可重複的例子，什麼基本上我試圖做的，正在創作5點估算的數據集然後應用SVM使用中插入符號火車功能各估算數據集，然後合奏使用caretEnsemble產生訓練模型。最後，我使用整體模型預測每個測試集。

不過，我得到這個錯誤

Error in check_bestpreds_obs(modelLibrary) :
Observed values for each component model are not the same. Please re-train the models with the same Y variable

有反正周圍，可以幫助我合奏不同的培訓模式？

任何幫助是真正的讚賞。

library(mice) 
    library(e1071) 
    library(caret) 
    library("caretEnsemble") 

data <- iris 
#Generate 10% missing values at Random 
iris.mis <- prodNA(iris, noNA = 0.1) 
#remove categorical variables 
iris.mis <- subset(iris.mis, select = -c(Species)) 

# 5 Imputation using mice pmm 

imp <- mice(iris.mis, m=5, maxit = 10, method = 'pmm', seed = 500) 

# save 5 imputed dataset. 
x1 <- complete(imp, action = 1, include = FALSE) 
x2 <- complete(imp, action = 2, include = FALSE) 
x3 <- complete(imp, action = 3, include = FALSE) 
x4 <- complete(imp, action = 4, include = FALSE) 
x5 <- complete(imp, action = 5, include = FALSE) 

## Apply the following method for each imputed set 

form <- iris$Sepal.Width # target column 
n <- nrow(x1) # since all data sample are the same length 
prop <- n%/%fold 
set.seed(7) 
newseq <- rank(runif(n)) 
k <- as.factor((newseq - 1)%/%prop + 1) 
CVfolds <- 10 


CVrepeats <- 3 
    indexPreds <- createMultiFolds(x1[k != i,]$Sepal.Width, CVfolds, CVrepeats) 
    ctrl <- trainControl(method = "repeatedcv", repeats = CVrepeats,number = CVfolds, returnResamp = "all", savePredictions = "all", index = indexPreds) 




fit1 <- train(Sepal.Width ~., data = x1[k !=i, ],method='svmLinear2',trControl = ctrl) 
fit2 <- train(Sepal.Width ~., data = x2[k != i, ],method='svmLinear2',trControl = ctrl) 
fit3 <- train(Sepal.Width ~., data = x3[k != i, ],method='svmLinear2',trControl = ctrl) 
fit4 <- train(Sepal.Width ~., data = x4[k != i, ],method='svmLinear2',trControl = ctrl) 
fit5 <- train(Sepal.Width ~., data = x5[k != i, ],method='svmLinear2',trControl = ctrl) 




#combine the created model to a list 
     svm.fit <- list(svmLinear1 = fit1, svmLinear2 = fit2, svmLinear3 = fit3, svmLinear4 = fit4, svmLinear5 = fit5) 

    # convert the list to cartlist 
    class(svm.fit) <- "caretList" 

    #create the ensemble where the error occur. 
    svm.all <- caretEnsemble(svm.fit,method='svmLinear2') 

Answer 1

你必須簡化你的例子。獲取錯誤不需要太多移動部件和循環。其中一個內部caretEnsemble控件拋出此錯誤，但該消息沒有很好定義。

這就是說， caretList需要有一個指定的trainControl對象，您使用每個火車模型。否則，重採樣會爲每個模型不同，你會得到錯誤：

"Component models do not have the same re-sampling strategies"

下一個問題是，你正在使用不同的數據集，每個列車對象。 CaretEnsemble旨在與相同的訓練數據集一起使用。即使他們有相同的基礎，你的x1到x5也是不同的。這將導致錯誤：

"Observed values for each component model are not the same. Please re-train the models with the same Y variable"

最後，如果你想從單獨訓練的模型構建一個model.list只使用c(model1, model2)。看到文檔c.train