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add doc for agaricus.test

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hetong 2014-09-06 21:54:12 -07:00
parent 43a781f59b
commit d174a79fbd
4 changed files with 12 additions and 10 deletions
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6
R-package/R/xgboost.R
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@ -69,10 +69,10 @@ xgboost <- function(data = NULL, label = NULL, params = list(), nrounds,
#'
#' \itemize{
#' \item \code{label} the label for each record
#' \item \code{data} a sparse Matrix of \code{dgCMatrix} class, with 127 rows.
#' \item \code{data} a sparse Matrix of \code{dgCMatrix} class, with 127 columns.
#' }
#'
#'#' @references
#' @references
#' https://archive.ics.uci.edu/ml/datasets/Mushroom
#'
#' Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository
@ -96,7 +96,7 @@ NULL
#'
#' \itemize{
#' \item \code{label} the label for each record
#' \item \code{data} a sparse Matrix of \code{dgCMatrix} class, with 127 rows.
#' \item \code{data} a sparse Matrix of \code{dgCMatrix} class, with 127 columns.
#' }
#'
#' @references

4
R-package/man/agaricus.train.Rd
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@ -17,10 +17,8 @@ This data set includes the following fields:
\itemize{
\item \code{label} the label for each record
\item \code{data} a sparse Matrix of \code{dgCMatrix} class, with 127 rows.
\item \code{data} a sparse Matrix of \code{dgCMatrix} class, with 127 columns.
}
#'
}
\references{
https://archive.ics.uci.edu/ml/datasets/Mushroom

2
R-package/man/xgb.DMatrix.save.Rd
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@ -6,7 +6,7 @@
xgb.DMatrix.save(DMatrix, fname)
}
\arguments{
\item{DMatrix}{the model object.}
\item{DMatrix}{the DMatrix object}
\item{fname}{the name of the binary file.}
}

10
R-package/man/xgboost.Rd
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@ -45,8 +45,12 @@ Parallelization is automatically enabled if OpenMP is present.
Number of threads can also be manually specified via "nthread" parameter
}
\examples{
data(iris)
bst <- xgboost(as.matrix(iris[,1:4]),as.numeric(iris[,5]=='setosa'), nrounds = 2)
pred <- predict(bst, as.matrix(iris[,1:4]))
data(agaricus.train, package='xgboost')
data(agaricus.test, package='xgboost')
train <- agaricus.train
test <- agaricus.test
bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
eta = 1, nround = 2,objective = "binary:logistic")
pred <- predict(bst, test$data)
}