From 32cbab1cc00e5640fd79fd8557c098128d7efbec Mon Sep 17 00:00:00 2001 From: david-cortes Date: Tue, 2 Jan 2024 08:20:51 +0100 Subject: [PATCH] [R] put 'verbose' in correct argument (#9942) --- R-package/R/xgb.train.R | 16 ++++++++-------- R-package/man/xgb.train.Rd | 16 ++++++++-------- 2 files changed, 16 insertions(+), 16 deletions(-) diff --git a/R-package/R/xgb.train.R b/R-package/R/xgb.train.R index d93a0643d1b3..e20c1af3e9fd 100644 --- a/R-package/R/xgb.train.R +++ b/R-package/R/xgb.train.R @@ -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: diff --git a/R-package/man/xgb.train.Rd b/R-package/man/xgb.train.Rd index 0ef2e2216d66..b2eaff27c4c1 100644 --- a/R-package/man/xgb.train.Rd +++ b/R-package/man/xgb.train.Rd @@ -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: