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[R] xgb.plot.tree fixes (#1939)

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* [R] a few fixes and improvements to xgb.plot.tree

* [R] deprecate n_first_tree replace with trees; fix types in xgb.model.dt.tree
This commit is contained in:
Vadim Khotilovich
2017-01-06 13:09:51 -06:00
committed by Tianqi Chen
parent d23ea5ca7d
commit d7406e07f3
7 changed files with 225 additions and 116 deletions
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20
R-package/man/xgb.plot.multi.trees.Rd
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@@ -8,9 +8,9 @@ xgb.plot.multi.trees(model, feature_names = NULL, features_keep = 5,
plot_width = NULL, plot_height = NULL, ...)
}
\arguments{
\item{model}{dump generated by the \code{xgb.train} function.}
\item{model}{produced by the \code{xgb.train} function.}
\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{feature_names}{names of each feature as a \code{character} vector.}
\item{features_keep}{number of features to keep in each position of the multi trees.}
@@ -27,21 +27,19 @@ Two graphs showing the distribution of the model deepness.
Visualization of the ensemble of trees as a single collective unit.
}
\details{
This function tries to capture the complexity of gradient boosted tree ensemble
in a cohesive way.
This function tries to capture the complexity of a gradient boosted tree model
in a cohesive way by compressing an ensemble of trees into a single tree-graph representation.
The goal is to improve the interpretability of a model generally seen as black box.
The goal is to improve the interpretability of the model generally seen as black box.
The function is dedicated to boosting applied to decision trees only.
The purpose is to move from an ensemble of trees to a single tree only.
Note: this function is applicable to tree booster-based models only.
It takes advantage of the fact that the shape of a binary tree is only defined by
its deepness (therefore in a boosting model, all trees have the same shape).
its depth (therefore, in a boosting model, all trees have similar shape).
Moreover, the trees tend to reuse the same features.
The function will project each tree on one, and keep for each position the
\code{features_keep} first features (based on Gain per feature measure).
The function projects each tree onto one, and keeps for each position the
\code{features_keep} first features (based on the Gain per feature measure).
This function is inspired by this blog post:
\url{https://wellecks.wordpress.com/2015/02/21/peering-into-the-black-box-visualizing-lambdamart/}