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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/man/a-compatibility-note-for-saveRDS-save.Rd
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@@ -43,6 +43,7 @@ bst2 <- xgb.load('xgb.model')
# 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)
@@ -58,5 +59,6 @@ saveRDS(obj, 'my_object.rds')
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')
}

2
R-package/man/xgb.cv.Rd
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@@ -154,7 +154,7 @@ The cross-validation process is then repeated \code{nrounds} times, with each of
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}
}
\examples{
data(agaricus.train, package='xgboost')

8
R-package/man/xgb.train.Rd
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@@ -215,16 +215,16 @@ User may set one or several \code{eval_metric} parameters.
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: