尝试将 Ranger 用于插入符号时出错

如何解决尝试将 Ranger 用于插入符号时出错

我有一个数据集“df_train”，其中包含我所有的解释变量和我的目标变量 (xxx1)。此外，我还有另一个数据集，其中包含拟合随机森林（xxx2 列）时要使用的权重。我正在尝试实现 3-fold cv，但似乎有些问题。它说的是类概率，但我正在尝试拟合回归随机森林。我不明白其余的错误是关于什么的。

train_control<- trainControl(method="cv",number=3,savePredictions = TRUE)


model2<- caret::train(xxx1~.,data=df_train,trControl=train_control,weights = train$xxx2,method="ranger",ntree = 64)

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   :6     NA's   :6     NA's   :6    
Error: Stopping
In addition: There were 20 warnings (use warnings() to see them)
> warnings()
Warning messages:
1: In train.default(x,y,weights = w,...) :
  cannnot compute class probabilities for regression
2: model fit Failed for Fold1: mtry= 2,min.node.size=5,splitrule=variance Error in ranger::ranger(dependent.variable.name = ".outcome",data = x,: 
  unused argument (ntree = 64)
3: model fit Failed for Fold1: mtry=32,: 
  unused argument (ntree = 64)
4: .....

解决方法

ntree 不是 Ranger 的参数。如果我设置的数据看起来像你的数据并且在没有 ntree 的情况下运行，它会起作用：

df_train = data.frame(matrix(rnorm(1000),ncol=10))
df_train$xxx1 = runif(100)

train = data.frame(xxx2 = runif(100))

model2<- caret::train(xxx1~.,data=df_train,trControl=train_control,weights = train$xxx2,method="ranger")

如果要设置树的数量，应该是num.trees =：

model2<- caret::train(xxx1~.,method="ranger",num.trees=64)

Random Forest 

100 samples
 10 predictor

No pre-processing
Resampling: Cross-Validated (3 fold) 
Summary of sample sizes: 67,67,66 
Resampling results across tuning parameters:

  mtry  splitrule   RMSE       Rsquared    MAE      
   2    variance    0.3003410  0.02482223  0.2519143
   2    extratrees  0.2947161  0.01832931  0.2468836
   6    variance    0.3044287  0.02300354  0.2558410
   6    extratrees  0.3006365  0.01630026  0.2523098
  10    variance    0.3167262  0.01966247  0.2662416
  10    extratrees  0.3023726  0.01428860  0.2530303