Visualizing a decision tree with pydot

If you would like to produce graphs, install the pydot library. Unfortunately, for Windows this installation could be non-trivial. Please focus on looking at the graphs rather than reproducing them if you struggle to install pydot.

How to do it...

1. Within an IPython Notebook, perform several imports and type the following script:

import numpy as np
from sklearn import tree
from sklearn.externals.six import StringIO
import pydot
from IPython.display import Image

dot_iris = StringIO() 
tree.export_graphviz(dtc, out_file = dot_iris, feature_names = iris.feature_names) 
graph = pydot.graph_from_dot_data(dot_iris.getvalue())
Image(graph.create_png())

How it works...

This is the decision tree that was produced with the training; calling the fit method on X_train and y_train. Look at it closely, starting at the top of the tree. You have 105 samples in the training set. The training set is split into three sets of 35 each: value = [35, 35, 35]. Explicitly, these...