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()
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()
Try this:
plt.figure(figsize=(12,12))
tree.plot_tree(clf, fontsize=10)
plt.show()
The problem is solved if you set the size before-hand :
from sklearn.tree import plot_tree, export_text
fig = plt.figure(figsize=(25,20))
_ = plot_tree(clf)
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