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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 #' # Both dataset are list with two items, a sparse matrix and labels
#' # (labels = outcome column which will be learned). #' # (labels = outcome column which will be learned).
#' # Each column of the sparse Matrix is a feature in one hot encoding format. #' # 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") #' eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
#' #'
#' # train$data@@Dimnames[[2]] represents the column names of the sparse matrix. #' # train$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 #' @examples
#' data(agaricus.train, package='xgboost') #' 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", #' eta = 1, nthread = 2, nround = 30, objective = "binary:logistic",
#' min_child_weight = 50) #' 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 #' #Both dataset are list with two items, a sparse matrix and labels
#' #(labels = outcome column which will be learned). #' #(labels = outcome column which will be learned).
#' #Each column of the sparse Matrix is a feature in one hot encoding format. #' #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") #' eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
#' #'
#' #agaricus.test$data@@Dimnames[[2]] represents the column names of the sparse matrix. #' #agaricus.test$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 # Both dataset are list with two items, a sparse matrix and labels
# (labels = outcome column which will be learned). # (labels = outcome column which will be learned).
# Each column of the sparse Matrix is a feature in one hot encoding format. # 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") eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
# train$data@Dimnames[[2]] represents the column names of the sparse matrix. # train$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{ \examples{
data(agaricus.train, package='xgboost') 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", eta = 1, nthread = 2, nround = 30, objective = "binary:logistic",
min_child_weight = 50) min_child_weight = 50)

3
R-package/man/xgb.plot.tree.Rd
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@ -43,9 +43,8 @@ data(agaricus.train, package='xgboost')
#Both dataset are list with two items, a sparse matrix and labels #Both dataset are list with two items, a sparse matrix and labels
#(labels = outcome column which will be learned). #(labels = outcome column which will be learned).
#Each column of the sparse Matrix is a feature in one hot encoding format. #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") eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
#agaricus.test$data@Dimnames[[2]] represents the column names of the sparse matrix. #agaricus.test$data@Dimnames[[2]] represents the column names of the sparse matrix.