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fix relative to examples #Rstat

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pommedeterresautee 2015-11-30 16:21:43 +01:00
parent 730bd72056
commit 2ca4016a1f
6 changed files with 6 additions and 10 deletions
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3
R-package/R/xgb.importance.R
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@ -48,9 +48,8 @@
#' # Both dataset are list with two items, a sparse matrix and labels
#' # (labels = outcome column which will be learned).
#' # Each column of the sparse Matrix is a feature in one hot encoding format.
#' train <- agaricus.train
#'
#' bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max.depth = 2,
#' eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
#'
#' # train$data@@Dimnames[[2]] represents the column names of the sparse matrix.

2
R-package/R/xgb.plot.deepness.R
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@ -93,7 +93,7 @@ get.paths.to.leaf <- function(dt.tree) {
#' @examples
#' data(agaricus.train, package='xgboost')
#'
#' bst <- xgboost(data = agaricus.train$data, max.depth = 15,
#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max.depth = 15,
#' eta = 1, nthread = 2, nround = 30, objective = "binary:logistic",
#' min_child_weight = 50)
#'

3
R-package/R/xgb.plot.tree.R
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@ -33,9 +33,8 @@
#' #Both dataset are list with two items, a sparse matrix and labels
#' #(labels = outcome column which will be learned).
#' #Each column of the sparse Matrix is a feature in one hot encoding format.
#' train <- agaricus.train
#'
#' bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max.depth = 2,
#' eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
#'
#' #agaricus.test$data@@Dimnames[[2]] represents the column names of the sparse matrix.

3
R-package/man/xgb.importance.Rd
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@ -54,9 +54,8 @@ data(agaricus.train, package='xgboost')
# Both dataset are list with two items, a sparse matrix and labels
# (labels = outcome column which will be learned).
# Each column of the sparse Matrix is a feature in one hot encoding format.
train <- agaricus.train
bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max.depth = 2,
eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
# train$data@Dimnames[[2]] represents the column names of the sparse matrix.

2
R-package/man/xgb.plot.deepness.Rd
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@ -35,7 +35,7 @@ This function is inspired by this blog post \url{http://aysent.github.io/2015/11
\examples{
data(agaricus.train, package='xgboost')
bst <- xgboost(data = agaricus.train$data, max.depth = 15,
bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max.depth = 15,
eta = 1, nthread = 2, nround = 30, objective = "binary:logistic",
min_child_weight = 50)

3
R-package/man/xgb.plot.tree.Rd
View File

@ -43,9 +43,8 @@ data(agaricus.train, package='xgboost')
#Both dataset are list with two items, a sparse matrix and labels
#(labels = outcome column which will be learned).
#Each column of the sparse Matrix is a feature in one hot encoding format.
train <- agaricus.train
bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max.depth = 2,
eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
#agaricus.test$data@Dimnames[[2]] represents the column names of the sparse matrix.