Plot a Single XGBoost Decision Tree

Question

I am using method on https://machinelearningmastery.com/visualize-gradient-boosting-decision-trees-xgboost-python/ to plot a XGBoost Decision Tree

from numpy import loadtxt
from xgboost import XGBClassifier
from xgboost import plot_tree
import matplotlib.pyplot as plt
# load data
dataset = loadtxt('pima-indians-diabetes.csv', delimiter=",")
# split data into X and y
X = dataset[:,0:8]
y = dataset[:,8]
# fit model no training data
model = XGBClassifier()
model.fit(X, y)
# plot single tree
plot_tree(model)
plt.show()

As I got 150 features,the plot looks quite small for all split points,how to draw a clear one or save in local place or any other ways/ideas could clearly show this ‘tree’ is quite appreciated

Answer 1

I had the same problem recently and the only way I found is by trying diffent figure size (it can still be bluery with big figure. For exemple, to plot the 4th tree, use:

fig, ax = plt.subplots(figsize=(30, 30))
xgb.plot_tree(model, num_trees=4, ax=ax)
plt.show()

To save it, you can do

plt.savefig("temp.pdf")

Also, each tree seperates two classes so you have as many tree as class.

Answer 2

To add to Serk's answer, you can also resize the figure before displaying it:

# ...
plot_tree(model)
plt.gcf().set_size_inches(18.5, 10.5)
plt.show()

Answer 3

You can try using the to_graphviz method instead - for me it results in a much more clear picture.

xgb.to_graphviz(xg_reg, num_trees=0, rankdir='LR')

However, most likely you will have issues with the size of that output.

In this case follow this: How can I specify the figsize of a graphviz representation of a decision tree?

Answer 4

I found this workaround on github, which also gives better images with the drawback that you have to open the .png file after.

xgb.plot_tree(bst, num_trees=2)
fig = matplotlib.pyplot.gcf()
fig.set_size_inches(150, 100)
fig.savefig('tree.png')