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regeneration of documentation

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El Potaeto 2014-12-29 23:56:31 +01:00
parent 755be4b846
commit 9b6a14a99d
2 changed files with 23 additions and 13 deletions
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17
R-package/man/xgb.dump.Rd
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@ -4,7 +4,7 @@
\alias{xgb.dump}
\title{Save xgboost model to text file}
\usage{
xgb.dump(model, fname, fmap = "")
xgb.dump(model, fname, fmap = "", with.stats = FALSE)
}
\arguments{
\item{model}{the model object.}
@ -12,11 +12,16 @@ xgb.dump(model, fname, fmap = "")
\item{fname}{the name of the binary file.}
\item{fmap}{feature map file representing the type of feature.
Detailed description could be found at
\url{https://github.com/tqchen/xgboost/wiki/Binary-Classification#dump-model}.
See demo/ for walkthrough example in R, and
\url{https://github.com/tqchen/xgboost/blob/master/demo/data/featmap.txt}
for example Format.}
Detailed description could be found at
\url{https://github.com/tqchen/xgboost/wiki/Binary-Classification#dump-model}.
See demo/ for walkthrough example in R, and
\url{https://github.com/tqchen/xgboost/blob/master/demo/data/featmap.txt}
for example Format.}
\item{with.stats}{whether dump statistics of splits
When this option is on, the model dump comes with two additional statistics:
gain is the approximate loss function gain we get in each split;
cover is the sum of second order gradient in each node.}
}
\description{
Save a xgboost model to text file. Could be parsed later.

19
R-package/man/xgb.importance.Rd
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@ -4,30 +4,35 @@
\alias{xgb.importance}
\title{Show importance of features in a model}
\usage{
xgb.importance(feature_names, filename_dump)
xgb.importance(feature_names = NULL, filename_dump = NULL)
}
\arguments{
\item{feature_names}{names of each feature as a character vector. Can be extracted from a sparse matrix.}
\item{feature_names}{names of each feature as a character vector. Can be extracted from a sparse matrix (see example). If model dump already contains feature names, this argument should be \code{NULL}.}
\item{filename_dump}{the name of the text file.}
\item{filename_dump}{the path to the text file storing the model.}
}
\description{
Read a xgboost model in text file format. Return a data.table of the features with their weight.
Read a xgboost model in text file format.
Can be tree or linear model (text dump of linear model are only supported in dev version of Xgboost for now).
}
\details{
Return a data.table of the features with their weight.
#'
}
\examples{
data(agaricus.train, package='xgboost')
data(agaricus.test, package='xgboost')
#Both dataset are list with two items, a sparse matrix and labels (outcome column which will be learned).
#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
test <- agaricus.test
bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
eta = 1, nround = 2,objective = "binary:logistic")
xgb.dump(bst, 'xgb.model.dump')
xgb.dump(bst, 'xgb.model.dump', with.stats = T)
#agaricus.test$data@Dimnames[[2]] represents the column name of the sparse matrix.
#agaricus.test$data@Dimnames[[2]] represents the column names of the sparse matrix.
xgb.importance(agaricus.test$data@Dimnames[[2]], 'xgb.model.dump')
}