[python] Visualizing decision tree in scikit-learn

I copy and change a part of your code as the below:

from pandas import read_csv, DataFrame
from sklearn import tree
from sklearn.tree import DecisionTreeClassifier
from os import system

data = read_csv('D:/training.csv')
Y = data.Y
X = data.ix[:,"X0":"X33"]

dtree = tree.DecisionTreeClassifier(criterion = "entropy")
dtree = dtree.fit(X, Y)

After making sure you have dtree, which means that the above code runs well, you add the below code to visualize decision tree:

Remember to install graphviz first: pip install graphviz

import graphviz 
from graphviz import Source
dot_data = tree.export_graphviz(dtree, out_file=None, feature_names=X.columns)
graph = graphviz.Source(dot_data) 
graph.render("name of file",view = True)

I tried with my data, visualization worked well and I got a pdf file viewed immediately.

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