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[R] docs update - callbacks and parameter style

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This commit is contained in:
Vadim Khotilovich
2016-06-27 01:59:58 -05:00
parent e9eb34fabc
commit a0aa305268
28 changed files with 564 additions and 162 deletions
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20
R-package/man/xgb.plot.multi.trees.Rd
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@@ -4,19 +4,21 @@
\alias{xgb.plot.multi.trees}
\title{Project all trees on one tree and plot it}
\usage{
xgb.plot.multi.trees(model, feature_names = NULL, features.keep = 5,
plot.width = NULL, plot.height = NULL)
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{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{features.keep}{number of features to keep in each position of the multi trees.}
\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_width}{width in pixels of the graph to produce}
\item{plot.height}{height in pixels of the graph to produce}
\item{plot_height}{height in pixels of the graph to produce}
\item{...}{currently not used}
}
\value{
Two graphs showing the distribution of the model deepness.
@@ -39,7 +41,7 @@ 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 tree on one, and keep for each position the
\code{features.keep} first features (based on Gain per feature measure).
\code{features_keep} first features (based on 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/}
@@ -47,11 +49,11 @@ This function is inspired by this blog post:
\examples{
data(agaricus.train, package='xgboost')
bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max.depth = 15,
eta = 1, nthread = 2, nround = 30, objective = "binary:logistic",
bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 15,
eta = 1, nthread = 2, nrounds = 30, objective = "binary:logistic",
min_child_weight = 50)
p <- xgb.plot.multi.trees(model = bst, feature_names = agaricus.train$data@Dimnames[[2]], features.keep = 3)
p <- xgb.plot.multi.trees(model = bst, feature_names = colnames(agaricus.train$data), features_keep = 3)
print(p)
}