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[R] Fix for cran submission of xgboost 0.6 (#1875)

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fix cran check
This commit is contained in:
Tong He 2016-12-15 12:04:54 -08:00 committed by GitHub
parent d943720883
commit 674024c53a
6 changed files with 7 additions and 7 deletions
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2
R-package/R/xgb.create.features.R
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@ -18,7 +18,7 @@
#' #'
#' International Workshop on Data Mining for Online Advertising (ADKDD) - August 24, 2014 #' International Workshop on Data Mining for Online Advertising (ADKDD) - August 24, 2014
#' #'
#' \url{https://research.facebook.com/publications/758569837499391/practical-lessons-from-predicting-clicks-on-ads-at-facebook/}. #' \url{https://research.fb.com/publications/practical-lessons-from-predicting-clicks-on-ads-at-facebook/}.
#' #'
#' Extract explaining the method: #' Extract explaining the method:
#' #'

2
R-package/R/xgb.dump.R
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@ -14,7 +14,7 @@
#' When this option is on, the model dump comes with two additional statistics: #' When this option is on, the model dump comes with two additional statistics:
#' gain is the approximate loss function gain we get in each split; #' gain is the approximate loss function gain we get in each split;
#' cover is the sum of second order gradient in each node. #' cover is the sum of second order gradient in each node.
#' @param dump_fomat either 'text' or 'json' format could be specified. #' @param dump_format either 'text' or 'json' format could be specified.
#' @param ... currently not used #' @param ... currently not used
#' #'
#' @return #' @return

2
R-package/R/xgb.train.R
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@ -119,7 +119,7 @@
#' \itemize{ #' \itemize{
#' \item \code{rmse} root mean square error. \url{http://en.wikipedia.org/wiki/Root_mean_square_error} #' \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{logloss} negative log-likelihood. \url{http://en.wikipedia.org/wiki/Log-likelihood}
#' \item \code{mlogloss} multiclass logloss. \url{https://www.kaggle.com/wiki/MultiClassLogLoss} #' \item \code{mlogloss} multiclass logloss. \url{https://www.kaggle.com/wiki/MultiClassLogLoss/}
#' \item \code{error} Binary classification error rate. It is calculated as \code{(# wrong cases) / (# all cases)}. #' \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. #' 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." #' Different threshold (e.g., 0.) could be specified as "error@0."

2
R-package/man/xgb.create.features.Rd
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@ -29,7 +29,7 @@ Joaquin Quinonero Candela)}
International Workshop on Data Mining for Online Advertising (ADKDD) - August 24, 2014 International Workshop on Data Mining for Online Advertising (ADKDD) - August 24, 2014
\url{https://research.facebook.com/publications/758569837499391/practical-lessons-from-predicting-clicks-on-ads-at-facebook/}. \url{https://research.fb.com/publications/practical-lessons-from-predicting-clicks-on-ads-at-facebook/}.
Extract explaining the method: Extract explaining the method:

4
R-package/man/xgb.dump.Rd
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@ -24,9 +24,9 @@ When this option is on, the model dump comes with two additional statistics:
gain is the approximate loss function gain we get in each split; gain is the approximate loss function gain we get in each split;
cover is the sum of second order gradient in each node.} cover is the sum of second order gradient in each node.}
\item{...}{currently not used} \item{dump_format}{either 'text' or 'json' format could be specified.}
\item{dump_fomat}{either 'text' or 'json' format could be specified.} \item{...}{currently not used}
} }
\value{ \value{
if fname is not provided or set to \code{NULL} the function will return the model as a \code{character} vector. Otherwise it will return \code{TRUE}. if fname is not provided or set to \code{NULL} the function will return the model as a \code{character} vector. Otherwise it will return \code{TRUE}.

2
R-package/man/xgb.train.Rd
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@ -174,7 +174,7 @@ The folloiwing is the list of built-in metrics for which Xgboost provides optimi
\itemize{ \itemize{
\item \code{rmse} root mean square error. \url{http://en.wikipedia.org/wiki/Root_mean_square_error} \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{logloss} negative log-likelihood. \url{http://en.wikipedia.org/wiki/Log-likelihood}
\item \code{mlogloss} multiclass logloss. \url{https://www.kaggle.com/wiki/MultiClassLogLoss} \item \code{mlogloss} multiclass logloss. \url{https://www.kaggle.com/wiki/MultiClassLogLoss/}
\item \code{error} Binary classification error rate. It is calculated as \code{(# wrong cases) / (# all cases)}. \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. 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." Different threshold (e.g., 0.) could be specified as "error@0."