46 lines
1.4 KiB
R
46 lines
1.4 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/xgb.plot.deepness.R
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\name{xgb.plot.deepness}
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\alias{xgb.plot.deepness}
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\title{Plot model trees deepness}
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\usage{
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xgb.plot.deepness(model = NULL)
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}
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\arguments{
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\item{model}{dump generated by the \code{xgb.train} function. Avoid the creation of a dump file.}
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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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Generate a graph to plot the distribution of deepness among trees.
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}
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\details{
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Display both the number of \code{leaf} and the distribution of \code{weighted observations}
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by tree deepness level.
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The purpose of this function is to help the user to find the best trade-off to set
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the \code{max.depth} and \code{min_child_weight} parameters according to the bias / variance trade-off.
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See \link{xgb.train} for more information about these parameters.
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The graph is made of two parts:
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\itemize{
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\item Count: number of leaf per level of deepness;
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\item Weighted cover: noramlized weighted cover per Leaf (weighted number of instances).
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}
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This function is inspired by this blog post \url{http://aysent.github.io/2015/11/08/random-forest-leaf-visualization.html}
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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, max.depth = 15,
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eta = 1, nthread = 2, nround = 30, objective = "binary:logistic",
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min_child_weight = 50)
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xgb.plot.deepness(model = bst)
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}
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