1. Data Mining: A Closer Look Chapter 2

2. 2.1 Data Mining Strategies (p35) Moh!

3. Classification Learning is supervised. The dependent variable is categorical. Well-defined classes. Current rather than future behavior.

4. Estimation Learning is supervised. The dependent variable is numeric. Well-defined output classes or variable. Current rather than future behavior.(???)

5. Prediction The emphasis is on predicting future rather than current outcomes. The output attribute may be categorical or numeric. The output variable must correspond to the variable to be predicted (the dependent variable). The input variables are the predictor variables, (or independent variables). Hence any supervised classification model, or supervised estimation model may be used for prediction if the variables are suitably chosen. That is: – if the output variable is “current” and – the input variables are previous attribute values If you can classify/estimate the present from the past, then you can predict the future from the present!!!

6. 3.5 Choosing a Data Mining Technique Initial Considerations Is learning supervised or unsupervised? Is explanation required? What is the interaction between input and output attributes? What are the data types of the input and output attributes?

7. Further Considerations which we might prefer to ignore Do We Know the Distribution of the Data? Do We Know Which Attributes Best Define the Data? Does the Data Contain Missing Values? Is Time an Issue? Which Technique Is Most Likely to Give a Best Test Set Accuracy?

8. Methods of Supervised classification Decision Trees Production Rules Instance based methods Multiple Discriminant Analysis Naïve Bayes methods Neural methods Today we consider only the first three, which are machine learning based; the last three are statistically based.

10. Concept Class = output variable = Healthy/Sick = 1/0

11. A Healthy Class Rule for the Cardiology Patient Dataset IF 169 <= Maximum Heart Rate <=202 THEN Concept Class = Healthy Rule accuracy: 85.07% High Heart rate is quite a good predictor of health Rule coverage: 34.55% But there are other ways of being healthy. – Rule accuracy is a between-class measure. – Rule coverage is a within-class measure. A Sick Class Rule for the Cardiology Patient Dataset IF Thal = Rev & Chest Pain Type = Asymptomatic THEN Concept Class = Sick Rule accuracy: 91.14% Rule coverage: 52.17% Healthy High Heart Rate

12. Acceptance/rejection of the “Life Insurance Promotion” offer is the output variable. A Hypothesis for the Insurance Promotion For credit card holders, A combination of one or more of the attributes can differentiate those who say yes to the life insurance promotion from those who say no.

13. 2.5 Evaluating Supervised Model Performance The Confusion Matrix A matrix used to summarize the results of a supervised classification. Entries along the main diagonal are correct classifications. Entries other than those on the main diagonal are classification errors.

14. True Classes c 11 is the number with true class “1” which are correctly classified as class “1” c 12 is the number with true class “1” which are mis-classified as class “2” Etc..

15. Two-Class Error Analysis Table 2.6 A Simple Confusion Matrix Computed Accept Reject Accept True False Accept Reject False True Accept Reject True

17. Figure 2.4 Targeted vs. mass mailing Comparing Models by Measuring Lift Representative sample Targetted Sample

18. Computing Lift

19. The population is 100,000. Consider the first Confusion Matrix. The acceptance rate of those predicted to accept is 540/23,460 = 2.3% The overall acceptance rate in the population is 1000/100,000 = 1% Therefore the lift in the response rate from using the classification model for targetted sampling/marketting is 2.3/1 = 2.3.

20. Basic Data Mining Techniques : Chapter 3 3.1 Decision Trees An Algorithm for Building Decision Trees 1. Let T be the set of training instances. 2. Choose an attribute that best differentiates the instances in T. 3. Create a tree node whose value is the chosen attribute. Create child links from this node where each link represents a unique value for the chosen attribute. Use the child link values to further subdivide the instances into subclasses. 4. For each subclass created in step 3: If the instances in the subclass satisfy predefined criteria or if the set of remaining attribute choices for this path is null, specify the classification for new instances following this decision path. If the subclass does not satisfy the criteria and there is at least one attribute to further subdivide the path of the tree, let T be the current set of subclass instances and return to step 2. Don’t worry too much about this. It is just “algorithm speak”, which we do not concern ourselves with.

21. 2.2/3.1 Supervised Data Mining Techniques Another (pseudo) Dataset

22. Figure 3.1 A partial decision tree with root node = income range

23. Figure 3.2 A partial decision tree with root node = credit card insurance

24. Figure 3.3 A partial decision tree with root node = age

25. Decision Trees for the Credit Card Promotion Database

26. Figure 3.4 A three-node decision tree for the credit card database

27. Figure 3.5 A two-node decision treee for the credit card database

29. Decision Tree Rules Rules for the Tree in Figure 3.4 IF Age <=43 & Sex = Male & Credit Card Insurance = No THEN Life Insurance Promotion = No IF Sex = Female & 19 <=Age <= 43 THEN Life Insurance Promotion = Yes Rule Accuracy: 100.00% Rule Coverage: 66.67%

30. A Production Rule for the Credit Card Promotion Database IF Sex = Female & 19 <=Age <= 43 THEN Life Insurance Promotion = Yes Rule Accuracy: 100.00% Rule Coverage: 66.67%

31. A Simplified Rule Obtained by Removing Attribute Age IF Sex = Male & Credit Card Insurance = No THEN Life Insurance Promotion = No

32. CART CHAID Other Methods for Building Decision Trees

33. Advantages of Decision Trees Easy to understand. Map nicely to a set of production rules. Applied to real problems. Make no prior assumptions about the data. Able to process both numerical and categorical data.

34. Disadvantages of Decision Trees Output attribute must be categorical. Limited to one output attribute. Decision tree algorithms are unstable. Trees created from numeric datasets can be complex.

35. An Excel-based Data Mining Tool Chapter 4 4.1 The iData Analyzer 4.2 ESX: A Multipurpose Tool for Data Mining A Live Demonstration Laboratory Exercise.

36. Figure 4.2 A successful installation

37. 4.5 A Six-Step Approach for Supervised Learning Step 1: Choose an Output Attribute Step 2: Perform the Mining Session Step 3: Read and Interpret Summary Results Step 4: Read and Interpret Test Set Results Step 5: Read and Interpret Class Results Step 6: Visualize and Interpret Class Rules

38. Figure 4.12 Test set instance classification Read and Interpret Test Set Results

39. 4.7 Instance Typicality The typicality of instance I is the “average” “similarity” of I to the other members of its cluster or class. definitions of “average” and “similarity” in iDA are secret!!! Typicality values lie between 0 and 1. 1 indicates the class “prototype” 0 indicates the class “outlier”

40. Typicality Scores Identify prototypical and outlier instances. Select a best set of training instances. Used to compute individual instance classification confidence scores. CLASS SIMILARITY is the average similarity of members of a class with other members of the same class.

41. Figure 4.13 Instance typicality

42. Other Definitions Given class C and categorical attribute A with values v1, v2,…vn, then the Class C predictability score for A = v2 (say) is the proportion of instances in C with A = v2. This is concerned with the predictability of A = v2 in the class C. Class C predictiveness is the proportion of instances with A = v2 which are in class C. This is concerned with the predictabiity of the Class C from A = v2.