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
59 lines
2.1 KiB
R
59 lines
2.1 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/xgb.plot.multi.trees.R
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\name{xgb.plot.multi.trees}
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\alias{xgb.plot.multi.trees}
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\title{Project all trees on one tree and plot it}
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\usage{
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xgb.plot.multi.trees(model, feature_names = NULL, features_keep = 5,
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plot_width = NULL, plot_height = NULL, ...)
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}
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\arguments{
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\item{model}{produced by the \code{xgb.train} function.}
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\item{feature_names}{names of each feature as a \code{character} vector.}
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\item{features_keep}{number of features to keep in each position of the multi trees.}
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\item{plot_width}{width in pixels of the graph to produce}
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\item{plot_height}{height in pixels of the graph to produce}
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\item{...}{currently not used}
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}
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\value{
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Two graphs showing the distribution of the model deepness.
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}
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\description{
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Visualization of the ensemble of trees as a single collective unit.
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}
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\details{
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This function tries to capture the complexity of a gradient boosted tree model
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in a cohesive way by compressing an ensemble of trees into a single tree-graph representation.
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The goal is to improve the interpretability of a model generally seen as black box.
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Note: this function is applicable to tree booster-based models only.
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It takes advantage of the fact that the shape of a binary tree is only defined by
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its depth (therefore, in a boosting model, all trees have similar shape).
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Moreover, the trees tend to reuse the same features.
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The function projects each tree onto one, and keeps for each position the
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\code{features_keep} first features (based on the Gain per feature measure).
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This function is inspired by this blog post:
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\url{https://wellecks.wordpress.com/2015/02/21/peering-into-the-black-box-visualizing-lambdamart/}
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}
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\examples{
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data(agaricus.train, package='xgboost')
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bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 15,
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eta = 1, nthread = 2, nrounds = 30, objective = "binary:logistic",
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min_child_weight = 50)
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p <- xgb.plot.multi.trees(model = bst, feature_names = colnames(agaricus.train$data),
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features_keep = 3)
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print(p)
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}
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