如何可视化sklearn GradientBoostingClassifier?

问题描述

我已经训练了

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?

解决方案

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 in your system
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:

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