1. A U S T R A L I A ’ S I N T E R N A T I O N A L U N I V E R S I T Y
COT5230 Data Mining
Week 7
Decision Trees
M O N A S H
A U S T R A L I A ’ S I N T E R N A T I O N A L U N I V E R S I T Y
Decision Trees

2. Outline Why use Decision Trees? What is a Decision Tree? Examples
Use as a data mining technique
Popular Models
CART
CHAID
ID3 & C4.5
Decision Trees

3. Why use Decision Trees? - 1
Whereas neural networks compute a mathematical function of their inputs to generate their outputs, decision trees use logical rules
IF the vehicle has a 2-door FRAME AND the vehicle has at least six CYLINDERS AND the BUYER is less than 50 years old AND the COST of the vehicle is >$30,000 AND the vehicle COLOUR is red THEN the BUYER is likely to be a MALE
Decision Trees

4. Why use Decision Trees? - 2
For some applications accuracy of classification or prediction is sufficient, e.g.:
Direct mail firm needing to find a model for identifying customers who will respond to mail
Predicting the stock market using past data
In other applications it is better (sometimes essential) that the decision be explained, e.g.:
Rejection of a credit application
Medical diagnosis
Humans generally require explanations for most decisions
Decision Trees

5. Why use Decision Trees? - 3
Example: When a bank rejects a credit card application, it is better to explain to the customer that it was due to the fact that:
He is not a permanent resident of Australia AND
He has been residing in Australia for < 6 months AND
He does not have a permanent job.
This is better than saying:
“We are very sorry, but our neural network thinks that you are not a credit-worthy customer !!!” (In which case the customer might become angry and move to another bank)
Decision Trees

6. What is a Decision Tree? - 1
Decision Trees

7. What is a Decision Tree? - 2
Built from root node (top) to leaf nodes (bottom)
A record first enters the root node
A test is applied to determine to which child node it should go next
A variety of algorithms for choosing the initial test exist. The aim is to discriminate best between the target classes
The process is repeated until a record arrives at a leaf node
The path from the root to a leaf node provides an expression of a rule
Decision Trees

8. Building a Decision Tree - 1
Algorithms for building decision trees (DTs) begin by trying to find the test which does the “best job” of splitting the data into the desired classes
The desired classes have to be identified at the start
Example: we need to describe the car purchasing profiles of males and females. The DT building algorithm examines the car purchase details database to find the best splitting criterion:
The DT algorithm may discover out that the No_of _doors variable is best for separating males and females
Engine type
No_of_doors
Age_of_customer
Colour
Cost
Type
Decision Trees

9. Building a Decision Tree - 2
The process is repeated to discover the best splitting criterion for the records assigned to each node
Once built, the effectiveness of a decision tree can be measured by applying it to a collection of previously unseen records and observing the percentage of correctly classified records
Engine type
No_of_doors 4 2
V6 V4
Decision Trees

10. Example - 1
Phone Technology
50 Churners
50 Non-churners
Requirement: Classify customers who churn (do not renew their phone contracts)
new
old
Time a Customer
30 Churners
50 Non-churners
20 Churners
0 Non-churners
<= 2.3 years
> 2.3 years
Age
25 Churners
10 Non-churners
5 Churners
40 Non-churners
<= 35
> 35
20 Churners
0 Non-churners
5 Churners
10 Non-churners
Decision Trees

11. Example - 2
The number of records in a given parent node equals the sum of the records contained in the child nodes
Quite easy to understand how the model is being built (unlike NNs)
Easy use the model
say for a target marketing campaign
Provides intuitive ideas about the customer base
e.g: “Customers who have been with the company for a couple of years and have new phones are pretty loyal”
Decision Trees

12. Use as a data mining technique - 1
Exploration
Analyzing the and splitting criteria selected by the algorithm may provide interesting insights which can be acted upon
e.g. if the following rule was identified: IF time a customer < 1.1 years AND sales channel = telesales THEN chance of churn is 65%
It might be worthwhile conducting a study on the way the telesales operators are making their calls
Decision Trees

13. Use as a data mining technique - 2
Exploration (continued)
Gleaning information from rules that fail
e.g. from the car example we obtained the rule: IF No_of_doors = 2 AND Engine_type = (>= V6) AND Customer_Age > 50 THEN there are almost no customers
Can this rule be useful?
A) Perhaps we can attempt to build up this non-existing market. If this is possible then we have the edge over competitors since we have a head start in this knowledge
B) We can remove these customers from our direct marketing campaign
Decision Trees

14. Use as a data mining technique - 3
Exploration (continued)
Again from the car example we noticed that:
There was no combination of rules to reliably discriminate between males and females for 4-door V4 engine vehicles
Do we consider this as an occasion where it was not possible to achieve our objective?
From this failure we have learnt that it is not important whether the customer is male or female for this category of vehicles
Perhaps we were asking the wrong question all along - this warrants further analysis
Decision Trees

15. Use as a data mining technique - 4
Data Pre-processing
Decision trees are very robust at handling different predictor types (number/categorical), and run quickly. Therefore the can be good for a first pass over the data in a data mining operation
This will create a subset of the possibly useful predictors which can then be fed into another model, say a neural network
Prediction
Once the decision tree is built it can be then be used as a prediction tool, by using on a set of new data
Decision Trees

16. Popular Decision Tree Models: CART - 1
CART: Classification & Regression Trees, developed in 1984 by a team of researchers (Leo Breiman et al.) from Stanford University
Used in the DM software Darwin - from Thinking Machines Corporation (recently bought by Oracle)
Uses an entropy metric to determine the split point (Information theory - Shannon 1949)
Measure of disorder = where p is is the probability of that prediction value occurring in a particular node of the tree
- CART produces a binary tree
Decision Trees

17. Popular Decision Tree Models: CART - 2
Consider the “Churn” problem from slide 7.10
At a certain node there are 100 customers to split
The algorithm will try each predictor using the measure of disorder (MOD)
For each predictor the algorithm will calculate the MOD for several values to identify the optimum
e.g: take age as the predictor and split the records according to age <=32 and age >32
100 total customers
30 who churn
70 who don’t churn
CART will select the predictor with the lowest MOD as the split point
Decision Trees

18. Node splitting A good split A bad split Name Churned? Name Churned?
Jim Yes Bob No
Sally Yes Betty No
Steve Yes Sue No
Joe Yes Alex No
A bad split
Steve No Sue Yes
Joe No Alex Yes
Decision Trees

19. Popular Decision Tree Models: CHAID
CHAID: Chi-squared Automatic Interaction Detector, developed by J. A. Hartigan in 1975.
Widely used since it is distributed as part of the popular statistical packages SAS and SPSS
Differs from CART in the way it identifies the split points. Instead of the information measure, it uses chi-squared test to identify the split points (a statistical measure used for identifying independent variables)
All predictors must be categorical or put into categorical form by binning
The accuracy of the two methods CHAID and CART have been found to be similar
Decision Trees

20. Popular Decision Tree Models: ID3 & C4.5
ID3: Iterative Dichtomiser, developed by the Australian researcher Ross Quinlan in 1979
Used in the data mining software Clementine of Integral Solutions Ltd. (taken over by SPSS)
-ID3 picks predictors and their splitting values on the basis of the gain in information provided
Gain is the difference between the amount of information that is needed to make a correct prediction both before and after the split has been made
If the amount of information required is much lower after the split is made, then the split is said to have decreased the disorder of the original data
Decision Trees

21. ID3 & C4.5 continued - 2 A B - + ---- +++++---- ++++ -
By using the entropy left right left entropy right entropy start entropy
A /5log(4/5)+ -4/5log(4/5)+ -5/10log(5/10) /5log(1/5)=.72 -1/5log(1/5)=.72 -5/10(log(5/10) = 1
B /9log(5/9)+ -1/1log(1/1) /9log(4/9)=.99 =0
Decision Trees

22. ID3 & C4.5 continued - 3 Split A will be selected
C4.5 introduces a number of extensions to ID3:
Handles unknown field values in training set
Tree pruning method
Automated rule generation
Weighted Entropy Gain
A (5/10)*0.72+(5/10)* = 0.72
B (9/10)*0.99+(1/10)* = 0.89
Decision Trees

23. Strengths and Weaknesses
Strengths of decision trees
Able to generate understandable rules
Classify with very little computation
Handle both continuous and categorical data
Provides a clear indication of which variables are most important for prediction or classification
Weaknesses
Not appropriate for estimation or prediction tasks (income, interest rates, etc.)
Problematic with time series data (much pre-processing required), can be computationally expensive
Decision Trees