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: 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

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()
`````` 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.  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()
``````