set.seed(2)
ind=sample(nrow(Boston),trunc(0.7*nrow(Boston)))
train=Boston[ind,]
test=Boston[-ind,]

# Fit lm model using 5 x 5-fold CV: model
model <- train(
 medv ~ ., train,
 method = "ranger",
 trControl = trainControl(
   method = "repeatedcv", number = 5,
   repeats = 5, verboseIter = F
 )
)

Random Forest

354 samples
 13 predictor

No pre-processing
Resampling: Cross-Validated (5 fold, repeated 5 times)
Summary of sample sizes: 282, 282, 285, 283, 284, 283, ...
Resampling results across tuning parameters:

  mtry  splitrule   RMSE      Rsquared   MAE
   2    variance    4.172443  0.8113023  2.702026
   2    extratrees  4.574969  0.7819608  2.946490
   7    variance    3.744418  0.8324785  2.475156
   7    extratrees  3.812538  0.8342013  2.478945
  13    variance    3.821406  0.8214275  2.517686
  13    extratrees  3.795269  0.8282988  2.465104

Tuning parameter 'min.node.size' was held constant at a value of 5
RMSE was used to select the optimal model using the smallest value.
The final values used for the model were mtry = 7, splitrule = variance and min.node.size = 5.

sqrt(mean((predict(model)-train$medv)^2)) #  1.487133
sqrt(mean((predict(model,newdata=test)-test$medv)^2)) # 2.648461