绘制单个 XGBoost 决策树 [英] Plot a Single XGBoost Decision Tree

问题描述

我在

解决方案

我最近遇到了同样的问题，我发现的唯一方法是尝试使用不同的图形大小(大图形仍然会变蓝.例如，绘制第四棵树，使用:

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

要保存它，你可以这样做

plt.savefig("temp.pdf")

此外，每棵树都将两个类分开，因此您拥有与类一样多的树.

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

解决方案

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.

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