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[1.5.2] [R] Fix broken links. (#7675)

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Jiaming Yuan 2022-02-20 01:04:56 +08:00 committed by GitHub
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6 changed files with 4 additions and 7 deletions
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1
R-package/DESCRIPTION
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@ -26,6 +26,7 @@ Authors@R: c(
person("Min", "Lin", role = c("aut")), person("Min", "Lin", role = c("aut")),
person("Yifeng", "Geng", role = c("aut")), person("Yifeng", "Geng", role = c("aut")),
person("Yutian", "Li", role = c("aut")), person("Yutian", "Li", role = c("aut")),
person("Jiaming", "Yuan", role = c("aut")),
person("XGBoost contributors", role = c("cph"), person("XGBoost contributors", role = c("cph"),
comment = "base XGBoost implementation") comment = "base XGBoost implementation")
) )

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.fb.com/publications/practical-lessons-from-predicting-clicks-on-ads-at-facebook/}. #' \url{https://research.facebook.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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@ -6,8 +6,6 @@
#' @param fname the name of the text file where to save the model text dump. #' @param fname the name of the text file where to save the model text dump.
#' If not provided or set to \code{NULL}, the model is returned as a \code{character} vector. #' If not provided or set to \code{NULL}, the model is returned as a \code{character} vector.
#' @param fmap feature map file representing feature types. #' @param fmap feature map file representing feature types.
#' Detailed description could be found at
#' \url{https://github.com/dmlc/xgboost/wiki/Binary-Classification#dump-model}.
#' See demo/ for walkthrough example in R, and #' See demo/ for walkthrough example in R, and
#' \url{https://github.com/dmlc/xgboost/blob/master/demo/data/featmap.txt} #' \url{https://github.com/dmlc/xgboost/blob/master/demo/data/featmap.txt}
#' for example Format. #' for example Format.

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.fb.com/publications/practical-lessons-from-predicting-clicks-on-ads-at-facebook/}. \url{https://research.facebook.com/publications/practical-lessons-from-predicting-clicks-on-ads-at-facebook/}.
Extract explaining the method: Extract explaining the method:

2
R-package/man/xgb.dump.Rd
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@ -20,8 +20,6 @@ xgb.dump(
If not provided or set to \code{NULL}, the model is returned as a \code{character} vector.} If not provided or set to \code{NULL}, the model is returned as a \code{character} vector.}
\item{fmap}{feature map file representing feature types. \item{fmap}{feature map file representing feature types.
Detailed description could be found at
\url{https://github.com/dmlc/xgboost/wiki/Binary-Classification#dump-model}.
See demo/ for walkthrough example in R, and See demo/ for walkthrough example in R, and
\url{https://github.com/dmlc/xgboost/blob/master/demo/data/featmap.txt} \url{https://github.com/dmlc/xgboost/blob/master/demo/data/featmap.txt}
for example Format.} for example Format.}

2
R-package/vignettes/discoverYourData.Rmd
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@ -138,7 +138,7 @@ levels(df[,Treatment])
Next step, we will transform the categorical data to dummy variables. Next step, we will transform the categorical data to dummy variables.
Several encoding methods exist, e.g., [one-hot encoding](https://en.wikipedia.org/wiki/One-hot) is a common approach. Several encoding methods exist, e.g., [one-hot encoding](https://en.wikipedia.org/wiki/One-hot) is a common approach.
We will use the [dummy contrast coding](https://stats.idre.ucla.edu/r/library/r-library-contrast-coding-systems-for-categorical-variables/) which is popular because it produces "full rank" encoding (also see [this blog post by Max Kuhn](http://appliedpredictivemodeling.com/blog/2013/10/23/the-basics-of-encoding-categorical-data-for-predictive-models)). We will use the [dummy contrast coding](https://stats.oarc.ucla.edu/r/library/r-library-contrast-coding-systems-for-categorical-variables/) which is popular because it produces "full rank" encoding (also see [this blog post by Max Kuhn](http://appliedpredictivemodeling.com/blog/2013/10/23/the-basics-of-encoding-categorical-data-for-predictive-models)).
The purpose is to transform each value of each *categorical* feature into a *binary* feature `{0, 1}`. The purpose is to transform each value of each *categorical* feature into a *binary* feature `{0, 1}`.