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New documentation rewording

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This commit is contained in:
Michaël Benesty
2015-12-09 18:26:56 +01:00
parent f761432c11
commit b2e68b8dc7
6 changed files with 20 additions and 32 deletions
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18
R-package/man/xgb.plot.tree.Rd
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@@ -8,7 +8,7 @@ xgb.plot.tree(feature_names = NULL, model = NULL, n_first_tree = NULL,
plot.width = NULL, plot.height = 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{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}.}
\item{model}{generated by the \code{xgb.train} function. Avoid the creation of a dump file.}
@@ -22,32 +22,26 @@ xgb.plot.tree(feature_names = NULL, model = NULL, n_first_tree = NULL,
A \code{DiagrammeR} of the model.
}
\description{
Read a tree model text dump.
Plotting only works for boosted tree model (not linear model).
Read a tree model text dump and plot the model.
}
\details{
The content of each node is organised that way:
\itemize{
\item \code{feature} value ;
\item \code{cover}: the sum of second order gradient of training data classified to the leaf, if it is square loss, this simply corresponds to the number of instances in that branch. Deeper in the tree a node is, lower this metric will be ;
\item \code{feature} value;
\item \code{cover}: the sum of second order gradient of training data classified to the leaf, if it is square loss, this simply corresponds to the number of instances in that branch. Deeper in the tree a node is, lower this metric will be;
\item \code{gain}: metric the importance of the node in the model.
}
Each branch finishes with a leaf. For each leaf, only the \code{cover} is indicated.
It uses \href{http://www.graphviz.org/}{GraphViz} library for that purpose.
The function uses \href{http://www.graphviz.org/}{GraphViz} library for that purpose.
}
\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")
#agaricus.test$data@Dimnames[[2]] represents the column names of the sparse matrix.
# agaricus.train$data@Dimnames[[2]] represents the column names of the sparse matrix.
xgb.plot.tree(feature_names = agaricus.train$data@Dimnames[[2]], model = bst)
}