PYTHON 决策树可视化

Question

[![enter image description here][1]][1]I want to visualize tree decision classifier that i have applied to my data in pdf or png file. I tried visualizing with graphviz via the code below:

X_train, X_test, y_train, y_test = \
        train_test_split(X, y, test_size=0.30, random_state=1)

clf =tree.DecisionTreeClassifier(max_depth=43)
clf = clf.fit(X_train, y_train)
from sklearn.externals.six import StringIO  
import pydot 
dot_data = StringIO() 
tree.export_graphviz(clf, out_file=dot_data) 
graph = pydot.graph_from_dot_data(dot_data.getvalue()) 
graph[0].write_pdf("tree.pdf") 

But the procedure can not be completed. Once i got the error of running out of memory and for the second time i got the error "dot stop working". Due to this issues i wanted to have an idea about the tree by knowing where is left children where is right children or left children? thanks for any response and help

Answer 1

If you are getting an error something like below:

Program terminated with status: -11. stderr follows: dot: graph is too large for cairo-renderer bitmaps.

Then to understand the tree, you can try to put it in a tree text format on screen like below:

from sklearn.tree import export_text

r = export_text(clf, feature_names=df_X_train.columns)
print(r)