What is difference between feval
and eval_metric
in xgb.train, both parametrs are only for evaluation purpose.
Post from Kaggle gives some insight :
https://www.kaggle.com/c/prudential-life-insurance-assessment/forums/t/18473/custom-objective-for-xgboost
They both do roughly the same thing.
Eval_metri
c can take a string (uses their internal functions) or user defined function
feval
only takes a function
Both are, as you noted, for evaluation purposes.
In the below examples you can see they are used very similarly.
## A simple xgb.train example:
param <- list(max_depth = 2, eta = 1, silent = 1, nthread = 2,
objective = "binary:logistic", eval_metric = "auc")
bst <- xgb.train(param, dtrain, nrounds = 2, watchlist)
## An xgb.train example where custom objective and evaluation metric are used:
logregobj <- function(preds, dtrain) {
labels <- getinfo(dtrain, "label")
preds <- 1/(1 + exp(-preds))
grad <- preds - labels
hess <- preds * (1 - preds)
return(list(grad = grad, hess = hess))
}
evalerror <- function(preds, dtrain) {
labels <- getinfo(dtrain, "label")
err <- as.numeric(sum(labels != (preds > 0)))/length(labels)
return(list(metric = "error", value = err))
}
# These functions could be used by passing them either:
# as 'objective' and 'eval_metric' parameters in the params list:
param <- list(max_depth = 2, eta = 1, silent = 1, nthread = 2,
objective = logregobj, eval_metric = evalerror)
bst <- xgb.train(param, dtrain, nrounds = 2, watchlist)
# or through the ... arguments:
param <- list(max_depth = 2, eta = 1, silent = 1, nthread = 2)
bst <- xgb.train(param, dtrain, nrounds = 2, watchlist,
objective = logregobj, eval_metric = evalerror)
# or as dedicated 'obj' and 'feval' parameters of xgb.train:
bst <- xgb.train(param, dtrain, nrounds = 2, watchlist,
obj = logregobj, feval = evalerror)
https://github.com/dmlc/xgboost/blob/72451457120ac9d59573cf7580ccd2ad178ef908/R-package/R/xgb.train.R#L176
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With