I have this simple code:

clf = tree.DecisionTreeClassifier()
clf = clf.fit(X, y)

tree.plot_tree(clf.fit(X, y))
plt.show()

And the result I get is this graph:
enter image description here

How do I make this graph legible? I’m using PyCharm Professional 2019.3 as my IDE.

I think the setting you are looking for is fontsize. You have to balance it with max_depth and figsize to get a readable plot. Here is an example

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()

enter image description here

If you want to capture structure of the whole tree I guess saving the plot with small font and high dpi is the solution. Then you can open a picture and zoom to the specific nodes to inspect them.

# create and train model
clf = tree.DecisionTreeClassifier()
clf.fit(X, y)

# save plot
plt.figure(figsize=(12,12))
tree.plot_tree(clf, fontsize=6)
plt.savefig('tree_high_dpi', dpi=100)

Here is an example of how it looks like on the bigger tree.

enter image description here

enter image description here

What about setting the size of the image before hand:

clf = tree.DecisionTreeClassifier()
clf = clf.fit(X, y)

fig, ax = plt.subplots(figsize=(10, 10))  # whatever size you want
tree.plot_tree(clf.fit(X, y), ax=ax)
plt.show()