R 插入符：结合 rfe() 和 train()

我想将递归特征消除与rfe()并与模型选择一起进行调整trainControl()使用该方法rf（随机森林）。我想要的是 MAPE（平均绝对百分比误差），而不是标准的汇总统计数据。因此我尝试使用以下代码ChickWeight数据集：

library(caret)
library(randomForest)
library(MLmetrics)

# Compute MAPE instead of other metrics
mape <- function(data, lev = NULL, model = NULL){
  mape <- MAPE(y_pred = data$pred, y_true = data$obs)
  c(MAPE = mape)
}

# specify trainControl
trc <- trainControl(method="repeatedcv", number=10, repeats=3, search="grid", savePred =T,
                    summaryFunction = mape)
# set up grid
tunegrid <- expand.grid(.mtry=c(1:3))

# specify rfeControl
rfec <- rfeControl(functions=rfFuncs, method="cv", number=10, saveDetails = TRUE)

set.seed(42)
results <- rfe(weight ~ Time + Chick + Diet, 
           sizes=c(1:3), # number of predictors from which should algorithm chose the best predictor
           data = ChickWeight, 
           method="rf",
           ntree = 250, 
           metric= "RMSE", 
           tuneGrid=tunegrid,
           rfeControl=rfec,
           trControl = trc)

代码运行没有错误。但是我在哪里可以找到 MAPE，我将其定义为summaryFunction in trainControl? Is trainControl被执行还是被忽略？

我如何重写代码以进行递归特征消除rfe然后调整超参数mtry using trainControl within rfe并同时计算附加误差测量（MAPE）？

trainControl被忽略，因为它的描述

控制计算的细微差别训练功能

会建议。要使用 MAPE，您需要

rfec$functions$summary <- mape

Then

rfe(weight ~ Time + Chick + Diet, 
    sizes = c(1:3),
    data = ChickWeight, 
    method ="rf",
    ntree = 250, 
    metric = "MAPE", # Modified
    maximize = FALSE, # Modified
    rfeControl = rfec)
#
# Recursive feature selection
#
# Outer resampling method: Cross-Validated (10 fold) 
#
# Resampling performance over subset size:
#
#  Variables   MAPE  MAPESD Selected
#          1 0.1903 0.03190         
#          2 0.1029 0.01727        *
#          3 0.1326 0.02136         
#         53 0.1303 0.02041         
#
# The top 2 variables (out of 2):
#    Time, Chick.L