Question

我想知道是否有某种方法可以将两种不同模型的预测相结合，建立在两种不同的输入特征集上。例如，首先是功能1:10，第二个是11:20，并与caretStack函数的caretEnssemble结合使用。

我在尝试：

 data("mtcars")

head(mtcars)

library(caret)
library(caretEnsemble)
library(glmnet)
library(gbm)

ma_control <- trainControl(method          = "cv",
                           number          = 2,
                           summaryFunction = RMSE,
                           verboseIter     = TRUE,
                           savePredictions = TRUE)

subset1                 <- mtcars[,c(2:3,1)]
subset2                 <- mtcars[,c(4:5,1)]

classification_formula1 <- as.formula(paste("mpg" ,"~",
                                            paste(names(subset1)[!names(subset1)=='mpg'],collapse="+")))
classification_formula2 <- as.formula(paste("mpg" ,"~",
                                            paste(names(subset2)[!names(subset2)=='mpg'],collapse="+")))

emf_tuneGrid_list <- NULL;
emf_tuneGrid_list$glmnet1_tuneGrid <- expand.grid(alpha = 1.0 ,lambda = 1)
emf_tuneGrid_list$gbm2_tuneGrid    <- expand.grid(interaction.depth = 1, n.trees = 101 ,
                                                    shrinkage = 0.5 , n.minobsinnode = 5)

emf_model_list <- caretList (
  trControl=ma_control, metric = "RMSE",
  tuneList=list(
    glmnet1= caretModelSpec(method='glmnet',  classification_formula = classification_formula1 , data = subset1 , tuneGrid=emf_tuneGrid_list$glmnet1_tuneGrid),
    gbm2   = caretModelSpec(method='gbm',     classification_formula = classification_formula2,  data = subset2 , tuneGrid=emf_tuneGrid_list$gbm2_tuneGrid , verbose = FALSE)
  )
)

但是在extractCaretTarget.default（...）中获取错误：争论＆＃34; y＆＃34;缺少，没有默认

R caretEnsemble在不同的特征子集上构建模型

0 个答案: