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Data Mining - CSE5230

CSE5230/DMS/2004/6. Data Mining - CSE5230. Classifiers 2 Decision Trees. Lecture Outline. Why use Decision Trees? What is a Decision Tree? Examples Use as a data mining technique Popular Models CART CHAID ID3 & C4.5. Lecture Objectives.

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Data Mining - CSE5230

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  1. CSE5230/DMS/2004/6 Data Mining - CSE5230 Classifiers 2 Decision Trees

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

  3. Lecture Objectives • By the end of this lecture you should be able to: • Describe what a Decision Tree is and how it is used to do classification • Explain how and why the explainability of Decision Tree classifications is important in some Data Mining tasks • Describe the top-down construction of a Decision Tree by choosing splitting criteria

  4. Why use Decision Trees? - 1 • Last week we talked about classifiers in general, and Bayesian classifiers in particular. Recapping: • A classifier assigns items to classes • There are many different classification models and algorithms used in Data Mining • Naïve Bayes, Decision Trees, Feedforward Neural Networks, etc. • Classification models differ in two important ways: • the complexity of the classifications they can learn • the ease with which humans can understand the reasons why certain items are classifier as they are

  5. Iris setosa Petal-length Petal-width Petal-length Sepal-length Why use Decision Trees? - 2 • Whereas Bayesian classifiers and neural networks compute a mathematical function of their inputs to generate their outputs, decision trees use logical rules, e.g.  2.6 > 2.6 IF Petal-length > 2.6 AND Petal-width  1.65 AND Petal-length > 5 AND Sepal-length > 6.05THEN the flower is Iris virginica NB. This is not the only rule for this species. What is the other? Figure adapted from [SGI2001]  1.65 > 1.65 Iris virginica  5 > 5 Iris versicolor  6.05 > 6.05 Iris versicolor Iris virginica

  6. Why use Decision Trees? - 3 • For some applications accuracy of classification or prediction is sufficient, e.g.: • A 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 explainable, e.g.: • Rejection of a credit card application • Medical diagnosis • Humans generally require explanations for most decisions • Sometimes there are legal and ethical requirements for this

  7. Why use Decision Trees? - 4 • 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/she is not a permanent resident of Australia ANDHe/she has been residing in Australia for < 6 months ANDHe/she 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)

  8. Sepal-length Iris setosa Petal-length Petal-width Petal-length What is a Decision Tree? root node • 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 exists. 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 test  2.6 > 2.6 child node  1.65 > 1.65 Iris virginica path  5 > 5 Iris versicolor  6.05 > 6.05 Iris versicolor Iris virginica leaf nodes

  9. 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 profiles of customers of a telephone company who “churn” (do not renew their contracts). The DT building algorithm examines the customer database to find the best splitting criterion: • The DT algorithm may discover out that the“Phone technology” variable is best for separating churners from non-churners Phone technology Age of customer Time has been a customer Gender

  10. 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 Phone technology old new Time has been a customer Churners  2.3 > 2.3

  11. Age Phone Technology Time has been a Customer 30 Churners 50 Non-churners 25 Churners 10 Non-churners 50 Churners 50 Non-churners Example - 1 • Requirement: Classifycustomers who churn,i.e. do not renewtheir phonecontracts.(adapted from [BeS1997]) new old 20 Churners 0 Non-churners <= 2.3 years > 2.3 years 5 Churners 40 Non-churners <= 35 > 35 20 Churners 0 Non-churners 5 Churners 10 Non-churners

  12. 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, as we will see next week) • Easy to use the model • e.g. for a targeted marketing campaign aimed at customers likely to churn • 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”

  13. Use as a data mining technique - 1 • Exploration • Analyzing the predictors 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 = telesalesTHEN chance of churn is 65% • It might be worthwhile conducting a study on the way the telesales operators are making their calls

  14. Use as a data mining technique - 2 • Exploration (continued) • Gleaning information from rules that fail • e.g. from the phone example we obtained the rule:IF Phone technology = old AND Time has been a customer  2.3 years AND Age > 35THEN there are only 15 customers (15% of total) • Can this rule be useful? • Perhaps we can attempt to build up this small market segment. If this is possible then we have the edge over competitors since we have a head start in this knowledge • We can remove these customers from our direct marketing campaign since there are so few of them

  15. Use as a data mining technique - 3 • Exploration (continued) • Again from the phone company example we noticed that: • There was no combination of rules to reliably discriminate between churners and non-churners for the small market segment mentioned on the previous slide (5 churners, 10 non-churners). • Do we consider this as an occasion where it was not possible to achieve our objective? • From this failure we have learnt that age is not all that important for this category churners (unlike those under 35). • Perhaps we were asking the wrong questions all along - this warrants further analysis

  16. 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 it on a new set of data

  17. Popular Decision Tree Models: CART • CART: Classification And 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)http://www.oracle.com/technology/documentation/darwin.html • Also available in SPSS Classification Trees add-on modulehttp://www.spss.com/classification_trees/ • Often uses an entropy measure to determine the split point (Shannon’s Information theory). measure of disorder (MOD) =where p is is the probability of that prediction value occurring in a particular node of the tree. Other measures used include Gini and twoing. • CART produces a binary tree

  18. CART - 2 • Consider the “Churn” problem from slide 6.11 • At the first node there are 100 customers to split, 50 who churn and 50 who don’t churnThe MOD of this node is: MOD = -0.5*log2(0.5) + -0.5*log2(0.5) = 1.00 • The algorithm will try each predictor variable • For each predictor the algorithm will calculate the MOD of the split produced by several values to identify the optimum • splitting on “Phone technology” produces two nodes, one with 50 churners and 30 non-churners, the other with 20 churners and 0 non-churners. The first of these has: MOD = -5/8*log2(5/8) + -3/8log2(3/8) = 0.95and the second has a MOD of 0. • CART will select the predictor producing nodes with the lowest MOD as the split point

  19. Node splitting An ideally good split Name Churned? Name Churned? Jim Yes Bob No Sally Yes Betty No Steve Yes Sue No Joe Yes Alex No An ideally bad split Name Churned? Name Churned? Jim Yes Bob No Sally Yes Betty No Steve No Sue Yes Joe No Alex Yes

  20. 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

  21. 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 information gain provided • Gain is the difference between the amount of information that is needed to make a correct prediction before a split has been made, and that required after the split • If the amount of information required is lower after the split is made, then the split is said to have decreased the disorder of the original data

  22. ID3 & C4.5 - 2

  23. ID3 & C4.5 - 3 • Split A will be selected

  24. ID3 & C4.5 - 4 • C4.5 introduces a number of extensions to ID3: • Handles unknown field values in training set • Tree pruning method • pruning produces a smaller tree (by “pruning” some of the leaves and branches) that performs better on test data • the aim is to avoid over-fitting the training data • Automated rule generation • extracts human-readable logical rules from the tree • Quinlan now has a commercial version with further improvements, See5/C5.0 • http://www.rulequest.com/see5-info.html

  25. Case Study • Now we will look at a case study, available on the web at http://web.maths.unsw.edu.au/~inge/statlearn/kolyshkina.pdf

  26. Strengths and Weaknesses • Strengths of decision trees • Able to generate understandable rules • Classify with very little computation • Some decision tree induction algorithms are able to handle both continuous and categorical data • Provides a clear indication of which variables are most important for prediction or classification • Weaknesses • Most are not appropriate for estimation or prediction tasks (income, interest rates, etc.) • exception is regression trees • Problematic with time series data (much pre-processing required), can be computationally expensive

  27. References • [BeL1997] J. A. Berry and G. Linoff, Data Mining Techniques: For Marketing, Sales, and Customer Support, John Wiley & Sons Inc.,1997 • [BeS1997] A. Berson and S. J. Smith, Data Warehousing, Data Mining and OLAP, McGraw Hill, 1997 • [KPR2002] I. Kolyshkina, P. Petocz and I. Rylande, Modeling insurance risk: A comparison of data mining and logistic regression approaches, in Proceedings of the 16th Australian Statistical Conference, Canberra, ACT, Australia, July 7-11, 2002.Related presentation: http://web.maths.unsw.edu.au/~inge/statlearn/kolyshkina.pdf • [SGI2001] Silicon Graphics Inc. MLC++ Utilities Manual, 2001http://www.sgi.com/tech/mlc/utils.html

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