update outdated docs

R-package/R/xgb.cv.R

@@ -91,8 +91,6 @@
 #'   \item \code{call} a function call.
 #'   \item \code{params} parameters that were passed to the xgboost library. Note that it does not
 #'         capture parameters changed by the \code{\link{xgb.cb.reset.parameters}} callback.
-#'   \item \code{callbacks} callback functions that were either automatically assigned or
-#'         explicitly passed.
 #'   \item \code{evaluation_log} evaluation history stored as a \code{data.table} with the
 #'         first column corresponding to iteration number and the rest corresponding to the
 #'         CV-based evaluation means and standard deviations for the training and test CV-sets.
@@ -101,14 +99,13 @@
 #'   \item \code{nfeatures} number of features in training data.
 #'   \item \code{folds} the list of CV folds' indices - either those passed through the \code{folds}
 #'         parameter or randomly generated.
-#'   \item \code{best_iteration} iteration number with the best evaluation metric value
-#'         (only available with early stopping).
-#'   \item \code{pred} CV prediction values available when \code{prediction} is set.
-#'         It is either vector or matrix (see \code{\link{xgb.cb.cv.predict}}).
-#'   \item \code{models} a list of the CV folds' models. It is only available with the explicit
-#'         setting of the \code{xgb.cb.cv.predict(save_models = TRUE)} callback.
 #' }
 #'
+#' Plus other potential elements that are the result of callbacks, such as a list `cv_predict` with
+#' a sub-element `pred` when passing `prediction = TRUE`, which adds the \link{xgb.cb.cv.predict}
+#' callback (note that one can also pass it manually under `callbacks` with different settings,
+#' such as saving also the models created during cross validation).
+#'
 #' @examples
 #' data(agaricus.train, package='xgboost')
 #' dtrain <- with(agaricus.train, xgb.DMatrix(data, label = label, nthread = 2))