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Fix CRAN submission (#6076)

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This commit is contained in:
Tong He
2020-09-02 14:38:27 +08:00
committed by GitHub
parent 884098ec22
commit 0cd0dad0b5
10 changed files with 23 additions and 19 deletions
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2
R-package/R/utils.R
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@@ -349,6 +349,7 @@ NULL
#' # Save as a stand-alone file (JSON); load it with xgb.load()
#' xgb.save(bst, 'xgb.model.json')
#' bst2 <- xgb.load('xgb.model.json')
#' if (file.exists('xgb.model.json')) file.remove('xgb.model.json')
#'
#' # Save as a raw byte vector; load it with xgb.load.raw()
#' xgb_bytes <- xgb.save.raw(bst)
@@ -364,6 +365,7 @@ NULL
#' obj2 <- readRDS('my_object.rds')
#' # Re-construct xgb.Booster object from the bytes
#' bst2 <- xgb.load.raw(obj2$xgb_model_bytes)
#' if (file.exists('my_object.rds')) file.remove('my_object.rds')
#'
#' @name a-compatibility-note-for-saveRDS-save
NULL

2
R-package/R/xgb.cv.R
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@@ -79,7 +79,7 @@
#'
#' All observations are used for both training and validation.
#'
#' Adapted from \url{http://en.wikipedia.org/wiki/Cross-validation_\%28statistics\%29#k-fold_cross-validation}
#' Adapted from \url{https://en.wikipedia.org/wiki/Cross-validation_\%28statistics\%29}
#'
#' @return
#' An object of class \code{xgb.cv.synchronous} with the following elements:

8
R-package/R/xgb.train.R
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@@ -130,16 +130,16 @@
#' Note that when using a customized metric, only this single metric can be used.
#' The following is the list of built-in metrics for which Xgboost provides optimized implementation:
#' \itemize{
#' \item \code{rmse} root mean square error. \url{http://en.wikipedia.org/wiki/Root_mean_square_error}
#' \item \code{logloss} negative log-likelihood. \url{http://en.wikipedia.org/wiki/Log-likelihood}
#' \item \code{rmse} root mean square error. \url{https://en.wikipedia.org/wiki/Root_mean_square_error}
#' \item \code{logloss} negative log-likelihood. \url{https://en.wikipedia.org/wiki/Log-likelihood}
#' \item \code{mlogloss} multiclass logloss. \url{https://scikit-learn.org/stable/modules/generated/sklearn.metrics.log_loss.html}
#' \item \code{error} Binary classification error rate. It is calculated as \code{(# wrong cases) / (# all cases)}.
#' By default, it uses the 0.5 threshold for predicted values to define negative and positive instances.
#' Different threshold (e.g., 0.) could be specified as "error@0."
#' \item \code{merror} Multiclass classification error rate. It is calculated as \code{(# wrong cases) / (# all cases)}.
#' \item \code{auc} Area under the curve. \url{http://en.wikipedia.org/wiki/Receiver_operating_characteristic#'Area_under_curve} for ranking evaluation.
#' \item \code{auc} Area under the curve. \url{https://en.wikipedia.org/wiki/Receiver_operating_characteristic#'Area_under_curve} for ranking evaluation.
#' \item \code{aucpr} Area under the PR curve. \url{https://en.wikipedia.org/wiki/Precision_and_recall} for ranking evaluation.
#' \item \code{ndcg} Normalized Discounted Cumulative Gain (for ranking task). \url{http://en.wikipedia.org/wiki/NDCG}
#' \item \code{ndcg} Normalized Discounted Cumulative Gain (for ranking task). \url{https://en.wikipedia.org/wiki/NDCG}
#' }
#'
#' The following callbacks are automatically created when certain parameters are set: