R 微调随机森林模型的超参数：树的数量

我正在使用caret包调优一个使用ranger的随机森林(RF)模型.因为在ranger包中我无法调整树的数量，所以我使用的是caret包.寻找最优树木数量的度量是R平方.我测试的树的范围是500到3000，步骤500(500,caret0,1500，...，3000).

The issue is that the R-squared is the same for every number of tree (see the attached image below):

我不认为这是正确的，所以我相信我的代码有问题.为什么我得到的是同样的R型平方？

以下是代码:

library(caret)
library(ranger)

# Load the data
block.data <- read.csv("path/block.data.csv")

eq1 = ntl ~ .

# Define the cross-validation method for hyperparameter tuning
control <- trainControl(method = "cv", number = 10, savePredictions = FALSE, 
                        search = 'grid', allowParallel = TRUE)

# default model
rf_default = train(eq1, 
                   data = block.data, 
                   method = "ranger", 
                   metric = "Rsquared", 
                   trControl = control)

print(rf_default)

# Define the grid of hyperparameters to be tuned
tuneGrid <- expand.grid(mtry = c(2, 3, 4, 5, 6, 7), # number of predictor variables to sample at each split
                        splitrule = c("variance", "extratrees"), # splitting rule
                        min.node.size = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)) # minimum size of terminal nodes
                       
# Train the model with hyperparameter tuning using caret
set.seed(234)
rf_model <- train(eq1, # formula for the response and predictors
                  data = block.data, 
                  method = "ranger", 
                  trControl = control, 
                  tuneGrid = tuneGrid) 

rf_model$bestTune

tuneGrid <- expand.grid(mtry = rf_model$bestTune$mtry,
                        splitrule = rf_model$bestTune$splitrule,
                        min.node.size = rf_model$bestTune$min.node.size)

store_maxtrees <- list()
for (ntree in c(500, 1000, 1500, 2000, 2500, 3000)) {
  set.seed(345)
  rf_maxtrees <- train(eq1,
                       data = block.data,
                       method = "ranger",
                       metric = "Rsquared",
                       tuneGrid = tuneGrid,
                       trControl = control,
                       ntree = ntree)
  key <- toString(ntree)
  store_maxtrees[[key]] <- rf_maxtrees
}
results_tree <- resamples(store_maxtrees)
summary(results_tree)