Question

我想使用插入符号训练一些模型，然后在调整了参数后，将caretEnsemble与各个模型进行比较。我正在波士顿房屋数据集上对此进行测试。到目前为止，我的代码是：

library(caret)
library(ranger)
library(randomForest)
library(caretEnsemble)
library(xgboost)
library(mlbench)
library(e1071)
library(GAMBoost)
library(quantregForest)
library(glmnet)

#load in boston housing dataset
data(BostonHousing)

df <- data.frame(BostonHousing)

#set random seed for reproduction
set.seed(54321)

#break into train and test
indexes <- createDataPartition(df$medv, times = 1, p = 0.7, list = FALSE)

train <- df[indexes,]
test <- df[-indexes,]

#set train control
my_control <- trainControl(method = "repeatedcv",
                             number = 10,
                             repeats = 3,
                             savePredictions = 'final',
                             allowParallel = T,
                             index = createResample(train$medv, 25))

#create the model list, tuneLength here should get tuning paramaters
model_list <- caretList(
  medv~., data=train,
  trControl=my_control,
  metric="RMSE",
  methodList=c("glm"),
  tuneList=list(
    ranger=caretModelSpec(method = 'ranger', tuneLength = 2),
    rf=caretModelSpec(method = 'rf', tuneLength = 2),
    quantile=caretModelSpec(method = 'qrf', tuneLength = 2),
    ridge=caretModelSpec(method = 'ridge', tuneLength = 2),
    bam=caretModelSpec(method = 'gamboost', tuneLength = 2),
    svm=caretModelSpec(method = 'svmPoly', tuneLength = 2)
  )
)

这时我得到了错误：

Something is wrong; all the RMSE metric values are missing:
      RMSE        Rsquared        MAE     
 Min.   : NA   Min.   : NA   Min.   : NA  
 1st Qu.: NA   1st Qu.: NA   1st Qu.: NA  
 Median : NA   Median : NA   Median : NA  
 Mean   :NaN   Mean   :NaN   Mean   :NaN  
 3rd Qu.: NA   3rd Qu.: NA   3rd Qu.: NA  
 Max.   : NA   Max.   : NA   Max.   : NA  
 NA's   :2     NA's   :2     NA's   :2    
Error: Stopping
In addition: There were 50 or more warnings (use warnings() to see the first 50)

我不明白为什么。如果一切正常，我想做的其余工作如下：

#set new seed
set.seed(101)

#set new train control
trainControl = trainControl(method="repeatedcv",
                            number=10,
                            repeats=3,
                            savePredictions='final',
                            allowParallel = T,
                            index = createResample(train$medv, 25))

#ensemble the models
greedy_ensemble <- caretEnsemble(
  model_list,
  metric="RMSE",
  trControl=trainControl)

summary(greedy_ensemble)


#predict on test data
stack_predicteds <- predict(greedy_ensemble, newdata=test)
head(stack_predicteds)

使用caretEnsemble创建集成模型时出错

0 个答案: