R：在随机森林中调整 mtry 时出错（回归）

Question

I have the following code to tune the mtry hyperparameter of a random forest regression model:

set.seed(42)

mtry <- 1:10

# Define train control
trControl <- trainControl(method = "cv",
                          number = 10,
                          search = "grid")

for (i in mtry) {
  rf_random <- train(Price.Gas~., data=data_train,
                 method = "rf",
                 mtry = i,
                 metric = "RMSE",
                 trControl = trControl)
}

However, I get the error (that actually repeats itself for different values of mtry) :

model fit failed for Fold01: mtry= 2 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
  formal argument "mtry" matched by multiple actual arguments

How can I make this work to test different mtry values?

Answer 1

by default caret would tune the mtry over a grid, see manual so you don't need use a loop, but instead define it in tuneGrid= :

library(caret)
set.seed(42)

data_train = data.frame(Price.Gas = rnorm(100),matrix(rnorm(1000),ncol=10))

trControl <- trainControl(method = "cv",number = 10)

rf_random <- train(Price.Gas~., data=data_train,
                   method = "rf",
                   tuneGrid = data.frame(mtry = 1:10),
                   metric = "RMSE",
                   trControl = trControl)

Random Forest 

100 samples
 10 predictor

No pre-processing
Resampling: Cross-Validated (10 fold) 
Summary of sample sizes: 89, 90, 91, 89, 91, 90, ... 
Resampling results across tuning parameters:

  mtry  RMSE       Rsquared   MAE      
   1    0.8556649  0.2122988  0.6921878
   2    0.8458829  0.2102749  0.6808978
   3    0.8518204  0.1975061  0.6909111
   4    0.8451160  0.1918390  0.6871511
   5    0.8386129  0.2037676  0.6808157
   6    0.8476718  0.1949056  0.6889514
   7    0.8434816  0.2082844  0.6833892
   8    0.8447137  0.1979602  0.6860908
   9    0.8419739  0.1960369  0.6825207
  10    0.8533284  0.1876459  0.6892574

RMSE was used to select the optimal model using the smallest value.
The final value used for the model was mtry = 5.