Solved – What are RMSE SD and Rsquared SD metrics in resampling results using R package:caret

caretresamplingrmsstandard deviation

I've been doing predictive modelling with R package caret. When resampling regression models, I get the traditional RMSE and Rsquared metrics, but also RMSE SD and Rsquared SD, for which I haven't found explanation in the manuals or documentations. Please, could anyone enlighten me?

Reproducible code:

library(mlbench)
data(BostonHousing)
library(caret)

ctrl <- trainControl(method = "cv", number = 2)
lmFit <- train(medv ~ ., data = BostonHousing, method = "lm", trControl = ctrl)
print(lmFit)

outputs:

Linear Regression 

506 samples
13 predictors

No pre-processing
Resampling: Cross-Validated (2 fold) 

Summary of sample sizes: 254, 252 

Resampling results

RMSE  Rsquared  RMSE SD  Rsquared SD
4.91  0.721     0.202    0.00304

Cheers

Maria

Best Answer

It is the standard deviation of the resamples:

> lmFit$resample
          RMSE  Rsquared Resample
    1 4.702857 0.7283872    Fold1
    2 5.266187 0.6838433    Fold2
> apply(lmFit$resample[, 1:2], 2, sd)
      RMSE   Rsquared 
0.39833479 0.03149727 
> lmFit
Linear Regression 

506 samples
 13 predictors

No pre-processing
Resampling: Cross-Validated (2 fold) 

Summary of sample sizes: 253, 253 

Resampling results

  RMSE  Rsquared  RMSE SD  Rsquared SD
  4.98  0.706     0.398    0.0315

Max

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