我认为您要查找的设置是 fontsize 。您必须将其与 max_depth 和 figsize 进行平衡，才能获得可读的图。下面是一个例子

from sklearn import tree
from sklearn.datasets import load_iris
import matplotlib.pyplot as plt
# load data
X, y = load_iris(return_X_y=True)
# create and train model
clf = tree.DecisionTreeClassifier(max_depth=4)  # set hyperparameter
clf.fit(X, y)
# plot tree
plt.figure(figsize=(12,12))  # set plot size (denoted in inches)
tree.plot_tree(clf, fontsize=10)
plt.show()

​

​

如果你想捕捉整个树的结构，我想用小字体和高dpi保存绘图是解决方案。然后，您可以打开图片并缩放到特定节点进行检查。

# create and train model
clf = tree.DecisionTreeClassifier()
clf.fit(X, y)
# save plot
plt.figure(figsize=(12,12))
tree.plot_tree(clf, fontsize=6)