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Cross validation documentation improvement

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pommedeterresautee 2015-03-17 23:18:00 +01:00
parent 240c314ac0
commit 33205d1fbd
2 changed files with 27 additions and 21 deletions
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25
R-package/R/xgb.cv.R
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@ -25,12 +25,12 @@
#' \item \code{nthread} number of thread used in training, if not set, all threads are used
#' }
#'
#' See \url{https://github.com/tqchen/xgboost/wiki/Parameters} for
#' further details. See also demo/ for walkthrough example in R.
#' @param data takes an \code{xgb.DMatrix} as the input.
#' See \link{xgb.train} for further details.
#' See also demo/ for walkthrough example in R.
#' @param data takes an \code{xgb.DMatrix} or \code{Matrix} as the input.
#' @param nrounds the max number of iterations
#' @param nfold number of folds used
#' @param label option field, when data is Matrix
#' @param nfold the original dataset is randomly partitioned into \code{nfold} equal size subsamples.
#' @param label option field, when data is \code{Matrix}
#' @param missing Missing is only used when input is dense matrix, pick a float
#' value that represents missing value. Sometime a data use 0 or other extreme value to represents missing values.
#' @param prediction A logical value indicating whether to return the prediction vector.
@ -56,18 +56,21 @@
#' @return A \code{data.table} with each mean and standard deviation stat for training set and test set.
#'
#' @details
#' This is the cross validation function for xgboost
#'
#' Parallelization is automatically enabled if OpenMP is present.
#' Number of threads can also be manually specified via "nthread" parameter.
#' The original sample is randomly partitioned into \code{nfold} equal size subsamples.
#'
#' This function only accepts an \code{xgb.DMatrix} object as the input.
#' Of the \code{nfold} subsamples, a single subsample is retained as the validation data for testing the model, and the remaining \code{nfold - 1} subsamples are used as training data.
#'
#' The cross-validation process is then repeated \code{nrounds} times, with each of the \code{nfold} subsamples used exactly once as the validation data.
#'
#' 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}
#'
#' @examples
#' data(agaricus.train, package='xgboost')
#' dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
#' history <- xgb.cv(data = dtrain, nround=3, nthread = 2, nfold = 5, metrics=list("rmse","auc"),
#' "max.depth"=3, "eta"=1, "objective"="binary:logistic")
#' max.depth =3, eta = 1, objective = "binary:logistic")
#' print(history)
#' @export
#'

23
R-package/man/xgb.cv.Rd
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@ -21,16 +21,16 @@ xgb.cv(params = list(), data, nrounds, nfold, label = NULL,
\item \code{nthread} number of thread used in training, if not set, all threads are used
}
See \url{https://github.com/tqchen/xgboost/wiki/Parameters} for
further details. See also demo/ for walkthrough example in R.}
See \link{xgb.train} for further details.
See also demo/ for walkthrough example in R.}
\item{data}{takes an \code{xgb.DMatrix} as the input.}
\item{data}{takes an \code{xgb.DMatrix} or \code{Matrix} as the input.}
\item{nrounds}{the max number of iterations}
\item{nfold}{number of folds used}
\item{nfold}{the original dataset is randomly partitioned into \code{nfold} equal size subsamples.}
\item{label}{option field, when data is Matrix}
\item{label}{option field, when data is \code{Matrix}}
\item{missing}{Missing is only used when input is dense matrix, pick a float
value that represents missing value. Sometime a data use 0 or other extreme value to represents missing values.}
@ -68,18 +68,21 @@ A \code{data.table} with each mean and standard deviation stat for training set
The cross valudation function of xgboost
}
\details{
This is the cross validation function for xgboost
The original sample is randomly partitioned into \code{nfold} equal size subsamples.
Parallelization is automatically enabled if OpenMP is present.
Number of threads can also be manually specified via "nthread" parameter.
Of the \code{nfold} subsamples, a single subsample is retained as the validation data for testing the model, and the remaining \code{nfold - 1} subsamples are used as training data.
This function only accepts an \code{xgb.DMatrix} object as the input.
The cross-validation process is then repeated \code{nrounds} times, with each of the \code{nfold} subsamples used exactly once as the validation data.
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}
}
\examples{
data(agaricus.train, package='xgboost')
dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
history <- xgb.cv(data = dtrain, nround=3, nthread = 2, nfold = 5, metrics=list("rmse","auc"),
"max.depth"=3, "eta"=1, "objective"="binary:logistic")
max.depth =3, eta = 1, objective = "binary:logistic")
print(history)
}