b4d97d3cb8
* [R] better argument check in xgb.DMatrix; fixes #1480 * [R] showsd was a dummy; fixes #2044 * [R] better categorical encoding explanation in vignette; fixes #1989 * [R] new roxygen version docs update
77 lines
3.0 KiB
R
77 lines
3.0 KiB
R
% Generated by roxygen2: do not edit by hand
|
|
% Please edit documentation in R/xgb.plot.tree.R
|
|
\name{xgb.plot.tree}
|
|
\alias{xgb.plot.tree}
|
|
\title{Plot a boosted tree model}
|
|
\usage{
|
|
xgb.plot.tree(feature_names = NULL, model = NULL, trees = NULL,
|
|
plot_width = NULL, plot_height = NULL, render = TRUE,
|
|
show_node_id = FALSE, ...)
|
|
}
|
|
\arguments{
|
|
\item{feature_names}{names of each feature as a \code{character} vector.}
|
|
|
|
\item{model}{produced by the \code{xgb.train} function.}
|
|
|
|
\item{trees}{an integer vector of tree indices that should be visualized.
|
|
If set to \code{NULL}, all trees of the model are included.
|
|
IMPORTANT: the tree index in xgboost model is zero-based
|
|
(e.g., use \code{trees = 0:2} for the first 3 trees in a model).}
|
|
|
|
\item{plot_width}{the width of the diagram in pixels.}
|
|
|
|
\item{plot_height}{the height of the diagram in pixels.}
|
|
|
|
\item{render}{a logical flag for whether the graph should be rendered (see Value).}
|
|
|
|
\item{show_node_id}{a logical flag for whether to include node id's in the graph.}
|
|
|
|
\item{...}{currently not used.}
|
|
}
|
|
\value{
|
|
When \code{render = TRUE}:
|
|
returns a rendered graph object which is an \code{htmlwidget} of class \code{grViz}.
|
|
Similar to ggplot objects, it needs to be printed to see it when not running from command line.
|
|
|
|
When \code{render = FALSE}:
|
|
silently returns a graph object which is of DiagrammeR's class \code{dgr_graph}.
|
|
This could be useful if one wants to modify some of the graph attributes
|
|
before rendering the graph with \code{\link[DiagrammeR]{render_graph}}.
|
|
}
|
|
\description{
|
|
Read a tree model text dump and plot the model.
|
|
}
|
|
\details{
|
|
The content of each node is organised that way:
|
|
|
|
\itemize{
|
|
\item Feature name.
|
|
\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 seen by a split
|
|
or collected by a leaf during training.
|
|
The deeper in the tree a node is, the lower this metric will be.
|
|
\item \code{Gain} (for split nodes): the information gain metric of a split
|
|
(corresponds to the importance of the node in the model).
|
|
\item \code{Value} (for leafs): the margin value that the leaf may contribute to prediction.
|
|
}
|
|
The tree root nodes also indicate the Tree index (0-based).
|
|
|
|
The "Yes" branches are marked by the "< split_value" label.
|
|
The branches that also used for missing values are marked as bold
|
|
(as in "carrying extra capacity").
|
|
|
|
This function uses \href{http://www.graphviz.org/}{GraphViz} as a backend of DiagrammeR.
|
|
}
|
|
\examples{
|
|
data(agaricus.train, package='xgboost')
|
|
|
|
bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 3,
|
|
eta = 1, nthread = 2, nrounds = 2,objective = "binary:logistic")
|
|
# plot all the trees
|
|
xgb.plot.tree(feature_names = colnames(agaricus.train$data), model = bst)
|
|
# plot only the first tree and include the node ID:
|
|
xgb.plot.tree(feature_names = colnames(agaricus.train$data), model = bst,
|
|
trees = 0, show_node_id = TRUE)
|
|
|
|
}
|