Hendrik/xgboost
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

documentation wording

Browse Source
This commit is contained in:
El Potaeto 2014-12-30 12:32:21 +01:00
parent 3694772bde
commit c754fd4ad0
1 changed files with 15 additions and 5 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

20
R-package/man/xgb.importance.Rd
View File

@ -9,15 +9,25 @@ 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 (see example). If model dump already contains feature names, this argument should be \code{NULL}.}
\item{filename_dump}{the path to the text file storing the model.}
\item{filename_dump}{the path to the text file storing the model. Model dump must include the gain per feature and per tree (\code{with.stats = T} in function \code{xgb.dump}).}
}
\description{
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).
Read a xgboost model text dump.
Can be tree or linear model (text dump of linear model are only supported in dev version of \code{Xgboost} for now).
Return a data.table of the features used in the model with their average gain (and their weight for boosted tree model) in the model.
}
\details{
Return a data.table of the features with their weight.
#'
This is the function to understand the model trained (and through your model, your data).
Results are returned for both linear and tree models.
\code{data.table} is returned by the function.
There are 3 columns :
\itemize{
\item \code{Features} name of the features as provided in \code{feature_names} or already present in the model dump.
\item \code{Gain} contribution of each feature to the model. For boosted tree model, each gain of each feature of each tree is taken into account, then average per feature to give a vision of the entire model. Highest percentage means most important feature regarding the \code{label} used for the training.
\item \code{Weight} percentage representing the relative number of times a feature have been taken into trees. \code{Gain} should be prefered to search the most important feature. For boosted linear model, this column has no meaning.
}
}
\examples{
data(agaricus.train, package='xgboost')