add doc for agaricus.test
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@ -69,10 +69,10 @@ xgboost <- function(data = NULL, label = NULL, params = list(), nrounds,
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#'
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#'
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#' \itemize{
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#' \itemize{
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#' \item \code{label} the label for each record
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#' \item \code{label} the label for each record
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#' \item \code{data} a sparse Matrix of \code{dgCMatrix} class, with 127 rows.
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#' \item \code{data} a sparse Matrix of \code{dgCMatrix} class, with 127 columns.
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#' }
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#' }
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#'
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#'
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#'#' @references
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#' @references
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#' https://archive.ics.uci.edu/ml/datasets/Mushroom
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#' https://archive.ics.uci.edu/ml/datasets/Mushroom
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#'
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#'
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#' Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository
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#' Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository
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@ -96,7 +96,7 @@ NULL
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#'
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#'
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#' \itemize{
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#' \itemize{
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#' \item \code{label} the label for each record
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#' \item \code{label} the label for each record
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#' \item \code{data} a sparse Matrix of \code{dgCMatrix} class, with 127 rows.
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#' \item \code{data} a sparse Matrix of \code{dgCMatrix} class, with 127 columns.
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#' }
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#' }
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#'
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#'
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#' @references
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#' @references
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@ -17,10 +17,8 @@ This data set includes the following fields:
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\itemize{
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\itemize{
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\item \code{label} the label for each record
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\item \code{label} the label for each record
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\item \code{data} a sparse Matrix of \code{dgCMatrix} class, with 127 rows.
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\item \code{data} a sparse Matrix of \code{dgCMatrix} class, with 127 columns.
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}
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}
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#'
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}
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}
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\references{
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\references{
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https://archive.ics.uci.edu/ml/datasets/Mushroom
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https://archive.ics.uci.edu/ml/datasets/Mushroom
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@ -6,7 +6,7 @@
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xgb.DMatrix.save(DMatrix, fname)
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xgb.DMatrix.save(DMatrix, fname)
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}
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}
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\arguments{
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\arguments{
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\item{DMatrix}{the model object.}
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\item{DMatrix}{the DMatrix object}
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\item{fname}{the name of the binary file.}
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\item{fname}{the name of the binary file.}
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}
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}
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@ -45,8 +45,12 @@ Parallelization is automatically enabled if OpenMP is present.
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Number of threads can also be manually specified via "nthread" parameter
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Number of threads can also be manually specified via "nthread" parameter
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}
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}
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\examples{
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\examples{
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data(iris)
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data(agaricus.train, package='xgboost')
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bst <- xgboost(as.matrix(iris[,1:4]),as.numeric(iris[,5]=='setosa'), nrounds = 2)
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data(agaricus.test, package='xgboost')
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pred <- predict(bst, as.matrix(iris[,1:4]))
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train <- agaricus.train
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test <- agaricus.test
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bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
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eta = 1, nround = 2,objective = "binary:logistic")
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pred <- predict(bst, test$data)
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}
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}
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