Python决策树的可视化

方法一：

# Fit the classifier with default hyper-parameters
clf = DecisionTreeClassifier(random_state=1234)
model = clf.fit(X, y)
text_representation = tree.export_text(clf)
print(text_representation)

|--- feature_2 <= 2.45
|   |--- class: 0
|--- feature_2 >  2.45
|   |--- feature_3 <= 1.75
|   |   |--- feature_2 <= 4.95
|   |   |   |--- feature_3 <= 1.65
|   |   |   |   |--- class: 1
|   |   |   |--- feature_3 >  1.65
|   |   |   |   |--- class: 2
|   |   |--- feature_2 >  4.95
|   |   |   |--- feature_3 <= 1.55
|   |   |   |   |--- class: 2
|   |   |   |--- feature_3 >  1.55
|   |   |   |   |--- feature_0 <= 6.95
|   |   |   |   |   |--- class: 1
|   |   |   |   |--- feature_0 >  6.95
|   |   |   |   |   |--- class: 2
|   |--- feature_3 >  1.75
|   |   |--- feature_2 <= 4.85
|   |   |   |--- feature_1 <= 3.10
|   |   |   |   |--- class: 2
|   |   |   |--- feature_1 >  3.10
|   |   |   |   |--- class: 1
|   |   |--- feature_2 >  4.85
|   |   |   |--- class: 2

方法二：

from dtreeviz.trees import dtreeviz # remember to load the package

viz = dtreeviz(clf, X, y,
                target_name="target",
                feature_names=iris.feature_names,
                class_names=list(iris.target_names))

viz

方法三：

fig = plt.figure(figsize=(25,20))
_ = tree.plot_tree(clf, 
                   feature_names=iris.feature_names,  
                   class_names=iris.target_names,
                   filled=True)

2. dict类型的树

import pydot

menu = {'dinner':
            {'chicken':'good',
             'beef':'average',
             'vegetarian':{
                   'tofu':'good',
                   'salad':{
                            'caeser':'bad',
                            'italian':'average'}
                   },
             'pork':'bad'}
        }

def draw(parent_name, child_name):
    edge = pydot.Edge(parent_name, child_name)
    graph.add_edge(edge)

def visit(node, parent=None):
    for k,v in node.iteritems():
        if isinstance(v, dict):
            # We start with the root node whose parent is None
            # we don't want to graph the None node
            if parent:
                draw(parent, k)
            visit(v, k)
        else:
            draw(parent, k)
            # drawing the label using a distinct name
            draw(k, k+'_'+v)

graph = pydot.Dot(graph_type='graph')
visit(menu)
graph.write_png('example1_graph.png')

---

参考：

1. blog；
2. Dictionary object to decision tree in Pydot;