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
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))