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

[R] docs update - callbacks and parameter style

Browse Source
This commit is contained in:
Vadim Khotilovich
2016-06-27 01:59:58 -05:00
parent e9eb34fabc
commit a0aa305268
28 changed files with 564 additions and 162 deletions
Download Patch File Download Diff File Expand all files Collapse all files

15
R-package/man/xgb.plot.tree.Rd
View File

@@ -5,7 +5,7 @@
\title{Plot a boosted tree model}
\usage{
xgb.plot.tree(feature_names = NULL, model = NULL, n_first_tree = NULL,
plot.width = NULL, plot.height = NULL)
plot_width = NULL, plot_height = NULL, ...)
}
\arguments{
\item{feature_names}{names of each feature as a \code{character} vector. Can be extracted from a sparse matrix (see example). If model dump already contains feature names, this argument should be \code{NULL}.}
@@ -14,9 +14,11 @@ xgb.plot.tree(feature_names = NULL, model = NULL, n_first_tree = NULL,
\item{n_first_tree}{limit the plot to the n first trees. If \code{NULL}, all trees of the model are plotted. Performance can be low for huge models.}
\item{plot.width}{the width of the diagram in pixels.}
\item{plot_width}{the width of the diagram in pixels.}
\item{plot.height}{the height of the diagram in pixels.}
\item{plot_height}{the height of the diagram in pixels.}
\item{...}{currently not used.}
}
\value{
A \code{DiagrammeR} of the model.
@@ -38,11 +40,10 @@ The function uses \href{http://www.graphviz.org/}{GraphViz} library for that pur
\examples{
data(agaricus.train, package='xgboost')
bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max.depth = 2,
eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 2,
eta = 1, nthread = 2, nrounds = 2,objective = "binary:logistic")
# agaricus.train$data@Dimnames[[2]] represents the column names of the sparse matrix.
xgb.plot.tree(feature_names = agaricus.train$data@Dimnames[[2]], model = bst)
xgb.plot.tree(feature_names = colnames(agaricus.train$data), model = bst)
}