diff --git a/R-package/man/xgb.importance.Rd b/R-package/man/xgb.importance.Rd
index 7d02315f9..883819993 100644
--- a/R-package/man/xgb.importance.Rd
+++ b/R-package/man/xgb.importance.Rd
@@ -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')