caretEnsmble与SVM（问题），不同的培训数据集

Question

以下是一个可重复的例子，什么基本上我试图做的，正在创作5点估算的数据集然后应用SVM使用中插入符号火车功能各估算数据集，然后合奏使用caretEnsemble产生训练模型。最后，我使用整体模型预测每个测试集。

不过，我得到这个错误

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

有反正周围，可以帮助我合奏不同的培训模式？

任何帮助是真正的赞赏。

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

你必须简化你的例子。获取错误不需要太多移动部件和循环。其中一个内部caretEnsemble控件抛出此错误，但该消息没有很好定义。

这就是说， caretList需要有一个指定的trainControl对象，您使用每个火车模型。否则，重采样会为每个模型不同，你会得到错误：

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

下一个问题是，你正在使用不同的数据集，每个列车对象。 CaretEnsemble旨在与相同的训练数据集一起使用。即使他们有相同的基础，你的x1到x5也是不同的。这将导致错误：

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

最后，如果你想从单独训练的模型构建一个model.list只使用c(model1, model2)。看到文档c.train