How to visualize an sklearn GradientBoostingClassifier?

Question

I've trained a gradient boost classifier, and I would like to visualize it using the graphviz_exporter tool shown here.

When I try it I get:

AttributeError: 'GradientBoostingClassifier' object has no attribute 'tree_'

this is because the graphviz_exporter is meant for decision trees, but I guess there's still a way to visualize it, since the gradient boost classifier must have an underlying decision tree.

Does anybody know how to do that?

Answer 1

The attribute estimators contains the underlying decision trees. The following code displays one of the trees of a trained GradientBoostingClassifier. Notice that although the ensemble is a classifier as a whole, each individual tree computes floating point values.

from sklearn.ensemble import GradientBoostingClassifier
from sklearn.tree import export_graphviz
import numpy as np

# Ficticuous data
np.random.seed(0)
X = np.random.normal(0,1,(1000, 3))
y = X[:,0]+X[:,1]*X[:,2] > 0

# Classifier
clf = GradientBoostingClassifier(max_depth=3, random_state=0)
clf.fit(X[:600], y[:600])

# Get the tree number 42
sub_tree_42 = clf.estimators_[42, 0]

# Visualization
# Install graphviz: https://www.graphviz.org/download/
from pydotplus import graph_from_dot_data
from IPython.display import Image
dot_data = export_graphviz(
    sub_tree_42,
    out_file=None, filled=True, rounded=True,
    special_characters=True,
    proportion=False, impurity=False, # enable them if you want
)
graph = graph_from_dot_data(dot_data)
Image(graph.create_png())

Tree number 42: