Caret package Custom metric

Question

I\'m using the caret function \"train()\" in one of my project and I\'d like to add a \"custom metric\" F1-score. I looked at this url caret package But I cannot understand

Answer 1

You should look at The caret Package - Alternate Performance Metrics for details. A working example:

library(caret)
library(MLmetrics)

set.seed(346)
dat <- twoClassSim(200)

## See https://topepo.github.io/caret/model-training-and-tuning.html#metrics
f1 <- function(data, lev = NULL, model = NULL) {
  f1_val <- F1_Score(y_pred = data$pred, y_true = data$obs, positive = lev[1])
  c(F1 = f1_val)
}

set.seed(35)
mod <- train(Class ~ ., data = dat,
             method = "rpart",
             tuneLength = 5,
             metric = "F1",
             trControl = trainControl(summaryFunction = f1, 
                                      classProbs = TRUE))

Answer 2

For the two-class case, you can try the following:

mod <- train(Class ~ ., 
             data = dat,
             method = "rpart",
             tuneLength = 5,
             metric = "F",
             trControl = trainControl(summaryFunction = prSummary, 
                                      classProbs = TRUE))

or define a custom summary function that combines both twoClassSummary and prSummary current favorite which provides the following possible evaluation metrics - AUROC, Spec, Sens, AUPRC, Precision, Recall, F - any of which can be used as the metric argument. This also includes the special case I mentioned in my comment on the accepted answer (F is NA).

comboSummary <- function(data, lev = NULL, model = NULL) {
  out <- c(twoClassSummary(data, lev, model), prSummary(data, lev, model))

  # special case missing value for F
  out$F <- ifelse(is.na(out$F), 0, out$F)  
  names(out) <- gsub("AUC", "AUPRC", names(out))
  names(out) <- gsub("ROC", "AUROC", names(out))
  return(out)
}

mod <- train(Class ~ ., 
             data = dat,
             method = "rpart",
             tuneLength = 5,
             metric = "F",
             trControl = trainControl(summaryFunction = comboSummary, 
                                      classProbs = TRUE))