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/R/xgb.train.R
View File

@@ -251,9 +251,9 @@
#' 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:
@@ -272,13 +272,13 @@
#'
#' # 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:
@@ -287,10 +287,10 @@
#'
#'
#' ## 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: