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

Wording improvement

Browse Source
This commit is contained in:
Michaël Benesty
2015-12-08 18:18:51 +01:00
parent ccd4b4be00
commit fbf2707561
6 changed files with 14 additions and 20 deletions
Download Patch File Download Diff File Expand all files Collapse all files

11
R-package/man/xgb.importance.Rd
View File

@@ -27,7 +27,7 @@ Create a \code{data.table} of the most important features of a model.
\details{
This is the function to understand the model trained (and through your model, your data).
Results are returned for both linear and tree models.
This function is for both linear and tree models.
\code{data.table} is returned by the function.
The columns are :
@@ -38,8 +38,9 @@ The columns are :
\item \code{Weight} percentage representing the relative number of times a feature have been taken into trees.
}
If you don't provide name, index of the features are used.
They are extracted from the boost dump (made on the C++ side), the index starts at 0 (usual in C++) instead of 1 (usual in R).
If you don't provide \code{feature_names}, index of the features will be used instead.
Because the index is extracted from the model dump (made on the C++ side), it starts at 0 (usual in C++) instead of 1 (usual in R).
Co-occurence count
------------------
@@ -53,10 +54,6 @@ If you need to remember one thing only: until you want to leave us early, don't
\examples{
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.
bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max.depth = 2,
eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")