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Polishing API + wording in function description #Rstat

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pommedeterresautee
2015-11-30 10:22:14 +01:00
parent 5e9f4dc973
commit 84ab71dd7e
8 changed files with 41 additions and 77 deletions
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18
R-package/man/xgb.plot.multi.trees.Rd
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@@ -10,13 +10,11 @@ xgb.plot.multi.trees(model, names, features.keep = 5, plot.width = NULL,
\arguments{
\item{model}{dump generated by the \code{xgb.train} function. Avoid the creation of a dump file.}
\item{features.keep}{number of features to keep in each position of the multi tree.}
\item{features.keep}{number of features to keep in each position of the multi trees.}
\item{plot.width}{width in pixels of the graph to produce}
\item{plot.height}{height in pixels of the graph to produce}
\item{filename_dump}{the path to the text file storing the model. Model dump must include the gain per feature and per tree (parameter \code{with.stats = T} in function \code{xgb.dump}).}
}
\value{
Two graphs showing the distribution of the model deepness.
@@ -25,21 +23,23 @@ 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 ensembles
This function tries to capture the complexity of gradient boosted tree ensemble
in a cohesive way.
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.
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 deepness (therefore in a boosting model, all trees have the same shape).
Moreover, the trees tend to reuse the same features.
The function will project each trees on one, and keep for each position the
\code{features.keep} first features (based on Gain per feature).
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).
This function is inspired from this blog post:
This function is inspired by this blog post:
\url{https://wellecks.wordpress.com/2015/02/21/peering-into-the-black-box-visualizing-lambdamart/}
}
\examples{