xgb.ggplot.deepness
From xgboost v0.6-3
by Tong He
Plot model trees deepness
Visualizes distributions related to depth of tree leafs.
xgb.plot.deepness
uses base R graphics, while xgb.ggplot.deepness
uses the ggplot backend.
Usage
xgb.ggplot.deepness(model = NULL, which = c("2x1", "max.depth", "med.depth", "med.weight"))
xgb.plot.deepness(model = NULL, which = c("2x1", "max.depth", "med.depth", "med.weight"), plot = TRUE, ...)
Arguments
- model
- either an
xgb.Booster
model generated by thexgb.train
function or a data.table result of thexgb.model.dt.tree
function. - which
- which distribution to plot (see details).
- plot
- (base R barplot) whether a barplot should be produced. If FALSE, only a data.table is returned.
- ...
- other parameters passed to
barplot
orplot
.
Details
When which="2x1"
, two distributions with respect to the leaf depth
are plotted on top of each other:
- the distribution of the number of leafs in a tree model at a certain depth;
- the distribution of average weighted number of observations ("cover") ending up in leafs at certain depth.
Those could be helpful in determining sensible ranges of the max_depth
and min_child_weight
parameters.
When which="max.depth"
or which="med.depth"
, plots of either maximum or median depth
per tree with respect to tree number are created. And which="med.weight"
allows to see how
a tree's median absolute leaf weight changes through the iterations.
This function was inspired by the blog post http://aysent.github.io/2015/11/08/random-forest-leaf-visualization.html.
Value
-
Other than producing plots (when
plot=TRUE
), the xgb.plot.deepness
function
silently returns a processed data.table where each row corresponds to a terminal leaf in a tree model,
and contains information about leaf's depth, cover, and weight (which is used in calculating predictions).The xgb.ggplot.deepness
silently returns either a list of two ggplot graphs when which="2x1"
or a single ggplot graph for the other which
options.
See Also
Examples
data(agaricus.train, package='xgboost')
# Change max_depth to a higher number to get a more significant result
bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 6,
eta = 0.1, nthread = 2, nrounds = 50, objective = "binary:logistic",
subsample = 0.5, min_child_weight = 2)
xgb.plot.deepness(bst)
xgb.ggplot.deepness(bst)
xgb.plot.deepness(bst, which='max.depth', pch=16, col=rgb(0,0,1,0.3), cex=2)
xgb.plot.deepness(bst, which='med.weight', pch=16, col=rgb(0,0,1,0.3), cex=2)
Community examples
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