Hello,

According to the documentation of the Caret package, the next chunks calculate the AUC metric in the context of cross-validation:

```
fitControl <- trainControl(method = "repeatedcv",
number = 10,
repeats = 10,
## Estimate class probabilities
classProbs = TRUE,
## Evaluate performance using
## the following function
summaryFunction = twoClassSummary)
set.seed(825)
gbmFit3 <- train(Class ~ ., data = training,
method = "gbm",
trControl = fitControl,
verbose = FALSE,
tuneGrid = gbmGrid,
## Specify which metric to optimize
metric = "ROC")
```

But I need to calculate the precision-recall curve, a more sensitive measure of classification performance when there are imbalanced classes. Could someone tell me if the chunks below are the right way to do it?

```
fitControl <- trainControl(method = "repeatedcv",
number = 10,
repeats = 10,
## Estimate class probabilities
classProbs = TRUE,
## Evaluate performance using
## the following function
summaryFunction = prSummary)
set.seed(825)
gbmFit3 <- train(Class ~ ., data = training,
method = "gbm",
trControl = fitControl,
verbose = FALSE,
tuneGrid = gbmGrid,
## Specify which metric to optimize
metric = "AUPRC")
```

Notice that only the *summaryFunction* and the **metric** arguments are changed.

I ask this because in Caret's documentation I didn't see any mention to the **metric = "AUPRC"** argument. Perhaps that argument is not necessary having **summaryFunction = prSummary** in the previous trainControl's chunk?

Thanks a lot!