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Decision Trees

Mar 31, 2026•5 min read•By Mohammed Vasim
AIMachine LearningLLMPyTorchTensorFlowGenerative AILangChainAI Agents

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Decision Trees

Estimated time needed: 15 minutes

Objectives

After completing this lab you will be able to:

  • Develop a classification model using Decision Tree Algorithm

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

Table of contents

  1. About the dataset
  2. Downloading the Data
  3. Pre-processing
  4. Setting up the Decision Tree
  5. Modeling
  6. Prediction
  7. Evaluation
  8. Visualization

Import the Following Libraries:

  • numpy (as np)
  • pandas
  • DecisionTreeClassifier from sklearn.tree

if you uisng you own version comment out

python
# Surpress warnings:
def warn(*args, **kwargs):
    pass
import warnings
warnings.warn = warn
python
!pip install matplotlib
!pip install pandas 
!pip install numpy 
%matplotlib inline
python
import matplotlib.pyplot as plt
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
import numpy as np

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 features of this dataset are Age, Sex, Blood Pressure, and the Cholesterol of the patients, and the target is the drug that each patient responded to.

It is a sample of multiclass 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 an unknown patient, or to prescribe a drug to a new patient.

Downloading the Data

To download the data, we will use pandas library to read itdirectly into a dataframe from IBM Object Storage.
python
my_data = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%203/data/drug200.csv', delimiter=",")
my_data.head()

Practice

What is the size of data?
python
# write your code here
Click here for the solution
python
my_data.shape

Pre_processing

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.

python
X = my_data[['Age', 'Sex', 'BP', 'Cholesterol', 'Na_to_K']].values
X[0:5]

As you may figure out, some features in this dataset are categorical, such as Sex or BP. Unfortunately, Sklearn Decision Trees does not handle categorical variables. We can still convert these features to numerical values using the LabelEncoder() method to convert the categorical variable into dummy/indicator variables.

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


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


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

X[0:5]

Now we can fill the target variable.

python
y = my_data["Drug"]
y[0:5]

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.
python
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.

python
X_trainset, X_testset, y_trainset, y_testset = train_test_split(X, y, test_size=0.3, random_state=3)

Practice

Print the shape of X_trainset and y_trainset. Ensure that the dimensions match.
python
# your code
Click here for the solution
python
print('Shape of X training set {}'.format(X_trainset.shape),'&',' Size of Y training set {}'.format(y_trainset.shape))

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

python
# your code
Click here for the solution
python
print('Shape of X test set {}'.format(X_testset.shape),'&','Size of y test set {}'.format(y_testset.shape))

Modeling

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.
python
drugTree = DecisionTreeClassifier(criterion="entropy", max_depth = 4)
drugTree # it shows the default parameters

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

python
drugTree.fit(X_trainset,y_trainset)

Prediction

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

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

python
print (predTree [0:5])
print (y_testset [0:5])

Evaluation

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

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 matches with the true set of labels, then the subset accuracy is 1.0; otherwise it is 0.0.

Visualization

Let's visualize the tree

python
# 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

#After executing the code below, a file named 'tree.png' would be generated which contains the decision tree image.
python
from sklearn.tree import export_graphviz
export_graphviz(drugTree, out_file='tree.dot', filled=True, feature_names=['Age', 'Sex', 'BP', 'Cholesterol', 'Na_to_K'])
!dot -Tpng tree.dot -o tree.png

Congratulations on completing the lab

Author

Saeed Aghabozorgi

Other Contributors

Joseph Santarcangelo

Richard Ye

© IBM Corporation. All rights reserved.

← PreviousSoftmax Regression, One-vs-All and One-vs-One for Multi-class ClassificationNext →Taxi Tip Prediction using Scikit-Learn and Snap ML

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