Machine Learning Coding Tutorial 2. Visualizing a Decision Tree

In the previous tutorial, we used decision tree as the classifier. Decision Tree is an easy to read and understand classifier. In this tutorial, we are going to write a program to visualize the decision tree.

1. “Iris” Problem

We are going to code a program to solve a classical machine learning problem called “Iris”, to identify what type of flowers base on the measurements: the length and width of the pedal, the length and width of the sepal.

Sample Iris Data
Sepal length Sepal width Petal length Petal width Species
5.1 3.5 1.4 0.2 I. setosa
4.9 3.0 1.4 0.2 I. setosa
4.7 3.2 1.3 0.2 I. setosa
5.7 2.9 4.2 1.3 I. versicolor
6.3 3.3 6.0 2.5 I. virginica

The Iris data set includes three types of flowers. They are all species of Iris: Setosa, Versicolor and Virginica.

Iris data is an array of arrays looks like this

 [[ 5.1  3.5  1.4  0.2]
  [ 4.9  3.0  1.4  0.2]
  [ 5.4  3.9  1.7  0.4]]

Iris data is an array of arrays looks like this

2. Procedure

Our coding procedure would be the following steps:

  1. Import Iris Dataset
  2. Create training and testing data
  3. Train a Classifier
  4. Predict label for a new flower
  5. Visualize the Decision Tree

3. Coding

Create a python file and write following code to program.

Please read comments carefully to understand the meaning of codes.

GoodTecher Machine Learning Coding Tutorial

"Iris" Machine Learning Program

The program takes a measurements (the length and width of the pedal and sepal) 
of a flower as input 
and predicts whether it is setosa, versicolor or virginica 

import numpy as np
from sklearn.datasets import load_iris
from sklearn import tree
import pydotplus

# load Iris dataset
iris = load_iris()

# picks some data from Iris dataset as test data
# and rest data would be training data
test_idx = [0, 10, 50, 100]

# training data
train_data = np.delete(, test_idx, axis = 0)
train_target = np.delete(, test_idx)

# testing data
test_data =[test_idx]
test_target =[test_idx]

# train classifier
clf = tree.DecisionTreeClassifier(), train_target)

# display and compare test target and predict target
print ("Test target: ")
print (test_target)
print ("clf.predict: ")
print (clf.predict(test_data))

# output Decision Tree procedure to a PDF file
dot_data = tree.export_graphviz(clf, out_file=None,
                         filled=True, rounded=True,
graph = pydotplus.graph_from_dot_data(dot_data)

Run the program with the following command in Terminal (Mac) or Command Prompt (Windows):


Do you see the program predicts the flower with a correct label?

Do you find the generated pdf file?

Yep. The machine is clever lol.

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