Decision Trees

In this lab exercise, you will learn a popular machine learning algorithm, Decision Tree. You will use this classification algorithm to build a model from historical data of patients, and their response to different medications. Then you use the trained decision tree to predict the class of a unknown patient, or to find a proper drug for a new patient.

Import the Following Libraries:

  • numpy (as np)
  • pandas
  • DecisionTreeClassifier from sklearn.tree
In [1]:
import numpy as np 
import pandas as pd
from sklearn.tree import DecisionTreeClassifier

About the dataset

Imagine that you are a medical researcher compiling data for a study. You have collected data about a set of patients, all of whom suffered from the same illness. During their course of treatment, each patient responded to one of 5 medications, Drug A, Drug B, Drug c, Drug x and y.

Part of your job is to build a model to find out which drug might be appropriate for a future patient with the same illness. The feature sets of this dataset are Age, Sex, Blood Pressure, and Cholesterol of patients, and the target is the drug that each patient responded to.

It is a sample of binary classifier, and you can use the training part of the dataset to build a decision tree, and then use it to predict the class of a unknown patient, or to prescribe it to a new patient.

Downloading the Data

To download the data, we will use !wget to download it from IBM Object Storage.
In [2]:
!wget -O drug200.csv
--2019-05-04 04:16:44--
Resolving (
Connecting to (||:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 6027 (5.9K) [text/csv]
Saving to: ‘drug200.csv’

drug200.csv         100%[===================>]   5.89K  --.-KB/s    in 0s      

2019-05-04 04:16:45 (64.1 MB/s) - ‘drug200.csv’ saved [6027/6027]

Did you know? When it comes to Machine Learning, you will likely be working with large datasets. As a business, where can you host your data? IBM is offering a unique opportunity for businesses, with 10 Tb of IBM Cloud Object Storage: Sign up now for free

now, read data using pandas dataframe:

In [3]:
my_data = pd.read_csv("drug200.csv", delimiter=",")
Age Sex BP Cholesterol Na_to_K Drug
0 23 F HIGH HIGH 25.355 drugY
1 47 M LOW HIGH 13.093 drugC
2 47 M LOW HIGH 10.114 drugC
3 28 F NORMAL HIGH 7.798 drugX
4 61 F LOW HIGH 18.043 drugY


What is the size of data?
In [4]:
# write your code here

(200, 6)


Using my_data as the Drug.csv data read by pandas, declare the following variables:

  • X as the Feature Matrix (data of my_data)
  • y as the response vector (target)

Remove the column containing the target name since it doesn't contain numeric values.

In [6]:
X = my_data[['Age', 'Sex', 'BP', 'Cholesterol', 'Na_to_K']].values
array([[23, 'F', 'HIGH', 'HIGH', 25.355],
       [47, 'M', 'LOW', 'HIGH', 13.093],
       [47, 'M', 'LOW', 'HIGH', 10.113999999999999],
       [28, 'F', 'NORMAL', 'HIGH', 7.797999999999999],
       [61, 'F', 'LOW', 'HIGH', 18.043]], dtype=object)

As you may figure out, some features in this dataset are categorical such as Sex or BP. Unfortunately, Sklearn Decision Trees do not handle categorical variables. But still we can convert these features to numerical values. pandas.get_dummies() Convert categorical variable into dummy/indicator variables.

In [7]:
from sklearn import preprocessing
le_sex = preprocessing.LabelEncoder()['F','M'])
X[:,1] = le_sex.transform(X[:,1]) 

le_BP = preprocessing.LabelEncoder()[ 'LOW', 'NORMAL', 'HIGH'])
X[:,2] = le_BP.transform(X[:,2])

le_Chol = preprocessing.LabelEncoder()[ 'NORMAL', 'HIGH'])
X[:,3] = le_Chol.transform(X[:,3]) 

array([[23, 0, 0, 0, 25.355],
       [47, 1, 1, 0, 13.093],
       [47, 1, 1, 0, 10.113999999999999],
       [28, 0, 2, 0, 7.797999999999999],
       [61, 0, 1, 0, 18.043]], dtype=object)

Now we can fill the target variable.

In [8]:
y = my_data["Drug"]
0    drugY
1    drugC
2    drugC
3    drugX
4    drugY
Name: Drug, dtype: object

Setting up the Decision Tree

We will be using train/test split on our decision tree. Let's import train_test_split from sklearn.cross_validation.
In [9]:
from sklearn.model_selection import train_test_split

Now train_test_split will return 4 different parameters. We will name them:
X_trainset, X_testset, y_trainset, y_testset

The train_test_split will need the parameters:
X, y, test_size=0.3, and random_state=3.

The X and y are the arrays required before the split, the test_size represents the ratio of the testing dataset, and the random_state ensures that we obtain the same splits.

In [10]:
X_trainset, X_testset, y_trainset, y_testset = train_test_split(X, y, test_size=0.3, random_state=3)


Print the shape of X_trainset and y_trainset. Ensure that the dimensions match

In [11]:
# your code
(140, 5)

Print the shape of X_testset and y_testset. Ensure that the dimensions match

In [12]:
# your code
(60, 5)


We will first create an instance of the DecisionTreeClassifier called drugTree.
Inside of the classifier, specify criterion="entropy" so we can see the information gain of each node.
In [13]:
drugTree = DecisionTreeClassifier(criterion="entropy", max_depth = 4)
drugTree # it shows the default parameters
DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=4,
            max_features=None, max_leaf_nodes=None,
            min_impurity_decrease=0.0, min_impurity_split=None,
            min_samples_leaf=1, min_samples_split=2,
            min_weight_fraction_leaf=0.0, presort=False, random_state=None,

Next, we will fit the data with the training feature matrix X_trainset and training response vector y_trainset

In [14]:,y_trainset)
DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=4,
            max_features=None, max_leaf_nodes=None,
            min_impurity_decrease=0.0, min_impurity_split=None,
            min_samples_leaf=1, min_samples_split=2,
            min_weight_fraction_leaf=0.0, presort=False, random_state=None,


Let's make some predictions on the testing dataset and store it into a variable called predTree.
In [15]:
predTree = drugTree.predict(X_testset)

You can print out predTree and y_testset if you want to visually compare the prediction to the actual values.

In [16]:
print (predTree [0:5])
print (y_testset [0:5])
['drugY' 'drugX' 'drugX' 'drugX' 'drugX']
40     drugY
51     drugX
139    drugX
197    drugX
170    drugX
Name: Drug, dtype: object


Next, let's import metrics from sklearn and check the accuracy of our model.
In [17]:
from sklearn import metrics
import matplotlib.pyplot as plt
print("DecisionTrees's Accuracy: ", metrics.accuracy_score(y_testset, predTree))
DecisionTrees's Accuracy:  0.9833333333333333

Accuracy classification score computes subset accuracy: the set of labels predicted for a sample must exactly match the corresponding set of labels in y_true.

In multilabel classification, the function returns the subset accuracy. If the entire set of predicted labels for a sample strictly match with the true set of labels, then the subset accuracy is 1.0; otherwise it is 0.0.


Can you calculate the accuracy score without sklearn ?

In [20]:
# your code here


Lets visualize the tree
In [ ]:
# Notice: You might need to uncomment and install the pydotplus and graphviz libraries if you have not installed these before
# !conda install -c conda-forge pydotplus -y
# !conda install -c conda-forge python-graphviz -y
In [21]:
from sklearn.externals.six import StringIO
import pydotplus
import matplotlib.image as mpimg
from sklearn import tree
%matplotlib inline 
In [22]:
dot_data = StringIO()
filename = "drugtree.png"
featureNames = my_data.columns[0:5]
targetNames = my_data["Drug"].unique().tolist()
out=tree.export_graphviz(drugTree,feature_names=featureNames, out_file=dot_data, class_names= np.unique(y_trainset), filled=True,  special_characters=True,rotate=False)  
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())  
img = mpimg.imread(filename)
plt.figure(figsize=(100, 200))
<matplotlib.image.AxesImage at 0x7f247fbb10b8>

Want to learn more?

IBM SPSS Modeler is a comprehensive analytics platform that has many machine learning algorithms. It has been designed to bring predictive intelligence to decisions made by individuals, by groups, by systems – by your enterprise as a whole. A free trial is available through this course, available here: SPSS Modeler

Also, you can use Watson Studio to run these notebooks faster with bigger datasets. Watson Studio is IBM's leading cloud solution for data scientists, built by data scientists. With Jupyter notebooks, RStudio, Apache Spark and popular libraries pre-packaged in the cloud, Watson Studio enables data scientists to collaborate on their projects without having to install anything. Join the fast-growing community of Watson Studio users today with a free account at Watson Studio

Thanks for completing this lesson!

Author: Saeed Aghabozorgi

Saeed Aghabozorgi, PhD is a Data Scientist in IBM with a track record of developing enterprise level applications that substantially increases clients’ ability to turn data into actionable knowledge. He is a researcher in data mining field and expert in developing advanced analytic methods like machine learning and statistical modelling on large datasets.

Copyright © 2018 Cognitive Class. This notebook and its source code are released under the terms of the MIT License.