Hendrik/xgboost
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

[R] put 'verbose' in correct argument (#9942)

Browse Source
This commit is contained in:
david-cortes
2024-01-02 08:20:51 +01:00
committed by GitHub
parent 73713de601
commit 32cbab1cc0
2 changed files with 16 additions and 16 deletions
Download Patch File Download Diff File Expand all files Collapse all files

16
R-package/man/xgb.train.Rd
View File

@@ -303,9 +303,9 @@ dtest <- with(
watchlist <- list(train = dtrain, eval = dtest)
## A simple xgb.train example:
param <- list(max_depth = 2, eta = 1, verbose = 0, nthread = nthread,
param <- list(max_depth = 2, eta = 1, nthread = nthread,
objective = "binary:logistic", eval_metric = "auc")
bst <- xgb.train(param, dtrain, nrounds = 2, watchlist)
bst <- xgb.train(param, dtrain, nrounds = 2, watchlist, verbose = 0)
## An xgb.train example where custom objective and evaluation metric are
## used:
@@ -324,13 +324,13 @@ evalerror <- function(preds, dtrain) {
# 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, verbose = 0, nthread = nthread,
param <- list(max_depth = 2, eta = 1, nthread = nthread,
objective = logregobj, eval_metric = evalerror)
bst <- xgb.train(param, dtrain, nrounds = 2, watchlist)
bst <- xgb.train(param, dtrain, nrounds = 2, watchlist, verbose = 0)
# or through the ... arguments:
param <- list(max_depth = 2, eta = 1, verbose = 0, nthread = nthread)
bst <- xgb.train(param, dtrain, nrounds = 2, watchlist,
param <- list(max_depth = 2, eta = 1, nthread = nthread)
bst <- xgb.train(param, dtrain, nrounds = 2, watchlist, verbose = 0,
objective = logregobj, eval_metric = evalerror)
# or as dedicated 'obj' and 'feval' parameters of xgb.train:
@@ -339,10 +339,10 @@ bst <- xgb.train(param, dtrain, nrounds = 2, watchlist,
## An xgb.train example of using variable learning rates at each iteration:
param <- list(max_depth = 2, eta = 1, verbose = 0, nthread = nthread,
param <- list(max_depth = 2, eta = 1, nthread = nthread,
objective = "binary:logistic", eval_metric = "auc")
my_etas <- list(eta = c(0.5, 0.1))
bst <- xgb.train(param, dtrain, nrounds = 2, watchlist,
bst <- xgb.train(param, dtrain, nrounds = 2, watchlist, verbose = 0,
callbacks = list(cb.reset.parameters(my_etas)))
## Early stopping: