1. Classification

2. 2 Classification: Definition  Given a collection of records (training set ) Each record contains a set of attributes, one of the attributes is the class.  Find a model for class attribute as a function of the values of other attributes.  Goal: previously unseen records should be assigned a class as accurately as possible. A test set is used to determine the accuracy of the model. Usually, the given data set is divided into training and test sets, with training set used to build the model and test set used to validate it.

3. 3 Illustrating Classification Task

4. 4 Examples of Classification Task  Predicting tumor cells as benign or malignant  Classifying credit card transactions as legitimate or fraudulent  Classifying secondary structures of protein as alpha-helix, beta-sheet, or random coil  Categorizing news stories as finance, weather, entertainment, sports, etc

5. 5 Classification Techniques  Decision Tree based Methods  Rule-based Methods  Memory based reasoning  Neural Networks  Naïve Bayes and Bayesian Belief Networks  Support Vector Machines

6. 6 Example of a Decision Tree categorical continuous class Refund MarSt TaxInc YES NO YesNo Married Single, Divorced < 80K> 80K Splitting Attributes Training Data Model: Decision Tree

7. 7 Another Example of Decision Tree categorical continuous class MarSt Refund TaxInc YES NO Yes No Married Single, Divorced < 80K> 80K There could be more than one tree that fits the same data!

8. 8 Decision Tree Classification Task Decision Tree

9. 9 Apply Model to Test Data Refund MarSt TaxInc YES NO YesNo Married Single, Divorced < 80K> 80K Test Data Start from the root of tree.

10. 10 Apply Model to Test Data Refund MarSt TaxInc YES NO YesNo Married Single, Divorced < 80K> 80K Test Data

11. 11 Apply Model to Test Data Refund MarSt TaxInc YES NO YesNo Married Single, Divorced < 80K> 80K Test Data

12. 12 Apply Model to Test Data Refund MarSt TaxInc YES NO YesNo Married Single, Divorced < 80K> 80K Test Data

13. 13 Apply Model to Test Data Refund MarSt TaxInc YES NO YesNo Married Single, Divorced < 80K> 80K Test Data

14. 14 Apply Model to Test Data Refund MarSt TaxInc YES NO YesNo Married Single, Divorced < 80K> 80K Test Data Assign Cheat to “No”

15. 15 Decision Tree Classification Task Decision Tree

16. 16 Decision Tree Induction  Many Algorithms: Hunt’s Algorithm (one of the earliest) CART ID3, C4.5 SLIQ,SPRINT

17. 17 General Structure of Hunt’s Algorithm  Let D t be the set of training records that reach a node t  General Procedure: If D t contains records that belong the same class y t, then t is a leaf node labeled as y t If D t is an empty set, then t is a leaf node labeled by the default class, y d If D t contains records that belong to more than one class, use an attribute test to split the data into smaller subsets. Recursively apply the procedure to each subset. DtDt ?

18. 18 Hunt’s Algorithm Don’t Cheat Refund Don’t Cheat Don’t Cheat YesNo Refund Don’t Cheat YesNo Marital Status Don’t Cheat Single, Divorced Married Taxable Income Don’t Cheat < 80K>= 80K Refund Don’t Cheat YesNo Marital Status Don’t Cheat Single, Divorced Married

19. 19 Tree Induction  Greedy strategy. Split the records based on an attribute test that optimizes certain criterion.  Issues Determine how to split the records  How to specify the attribute test condition?  How to determine the best split? Determine when to stop splitting

20. 20 How to Specify Test Condition?  Depends on attribute types Nominal Ordinal Continuous  Depends on number of ways to split 2-way split Multi-way split

21. 21 Splitting Based on Nominal Attributes  Multi-way split: Use as many partitions as distinct values.  Binary split: Divides values into two subsets. Need to find optimal partitioning. CarType Family Sports Luxury CarType {Family, Luxury} {Sports} CarType {Sports, Luxury} {Family} OR

22. 22  Multi-way split: Use as many partitions as distinct values.  Binary split: Divides values into two subsets. Need to find optimal partitioning.  What about this split? Splitting Based on Ordinal Attributes Size Small Medium Large Size {Medium, Large} {Small} Size {Small, Medium} {Large} OR Size {Small, Large} {Medium}

23. 23 Splitting Based on Continuous Attributes  Different ways of handling Discretization to form an ordinal categorical attribute  Static – discretize once at the beginning  Dynamic – ranges can be found by equal interval bucketing, equal frequency bucketing (percentiles), or clustering. Binary Decision: (A < v) or (A  v)  consider all possible splits and finds the best cut  can be more compute intensive

24. 24 Splitting Based on Continuous Attributes

25. 25 How to determine the Best Split Before Splitting: 10 records of class 0, 10 records of class 1 Which test condition is the best?

26. 26 How to determine the Best Split  Greedy approach: Nodes with homogeneous class distribution are preferred  Need a measure of node impurity: Non-homogeneous, High degree of impurity Homogeneous, Low degree of impurity

27. 27 Measures of Node Impurity  Gini Index  Entropy  Misclassification error

28. 28 Measure of Impurity: GINI  Gini Index for a given node t : (NOTE: p( j | t) is the relative frequency of class j at node t). Maximum (1 - 1/n c ) when records are equally distributed among all classes, implying least interesting information Minimum (0.0) when all records belong to one class, implying most interesting information

29. 29 Examples for computing GINI P(C1) = 0/6 = 0 P(C2) = 6/6 = 1 Gini = 1 – P(C1) 2 – P(C2) 2 = 1 – 0 – 1 = 0 P(C1) = 1/6 P(C2) = 5/6 Gini = 1 – (1/6) 2 – (5/6) 2 = 0.278 P(C1) = 2/6 P(C2) = 4/6 Gini = 1 – (2/6) 2 – (4/6) 2 = 0.444

30. 30 Alternative Splitting Criteria based on INFO  Entropy at a given node t: (NOTE: p( j | t) is the relative frequency of class j at node t). Measures homogeneity of a node.  Maximum (log n c ) when records are equally distributed among all classes implying least information  Minimum (0.0) when all records belong to one class, implying most information Entropy based computations are similar to the GINI index computations

31. 31 Examples for computing Entropy P(C1) = 0/6 = 0 P(C2) = 6/6 = 1 Entropy = – 0 log 0 – 1 log 1 = – 0 – 0 = 0 P(C1) = 1/6 P(C2) = 5/6 Entropy = – (1/6) log 2 (1/6) – (5/6) log 2 (5/6) = 0.65 P(C1) = 2/6 P(C2) = 4/6 Entropy = – (2/6) log 2 (2/6) – (4/6) log 2 (4/6) = 0.92

32. 32 Splitting Criteria based on Classification Error  Classification error at a node t :  Measures misclassification error made by a node.  Maximum (1 - 1/n c ) when records are equally distributed among all classes, implying least interesting information  Minimum (0.0) when all records belong to one class, implying most interesting information

33. 33 Examples for Computing Error P(C1) = 0/6 = 0 P(C2) = 6/6 = 1 Error = 1 – max (0, 1) = 1 – 1 = 0 P(C1) = 1/6 P(C2) = 5/6 Error = 1 – max (1/6, 5/6) = 1 – 5/6 = 1/6 P(C1) = 2/6 P(C2) = 4/6 Error = 1 – max (2/6, 4/6) = 1 – 4/6 = 1/3

34. 34 Example: Splitting Based on ENTROPY  Information Gain: Parent Node, p is split into k partitions; n i is number of records in partition i Measures Reduction in Entropy achieved because of the split. Choose the split that achieves most reduction (maximizes GAIN) Goal: maximize the GAIN Used in ID3 and C4.5 Disadvantage: Tends to prefer splits that result in large number of partitions, each being small but pure.

35. 35 Computing GAIN Split on Refund: Entropy(Refund=Yes) = 0 Entropy(Refund=No) = -(2/6)log(2/6) – (4/6)log(4/6) = 0.9183 Entropy(Children) = 0.3 (0) + 0.6 (0.9183) = 0.551 Gain = 0.9  (0.8813 – 0.551) = 0.3303 Missing value Before Splitting: Entropy(Parent) = -0.3 log(0.3)-(0.7)log(0.7) = 0.8813

36. 36 Splitting Based on ENTROPY  Gain Ratio: Parent Node, p is split into k partitions n i is the number of records in partition i Adjusts Information Gain by the entropy of the partitioning (SplitINFO). Higher entropy partitioning (large number of small partitions) is penalized! Used in C4.5 Designed to overcome the disadvantage of Information Gain

37. 37 Stopping Criteria for Tree Induction  Stop expanding a node when all the records belong to the same class  Stop expanding a node when all the records have similar attribute values  Early termination

38. 38 Decision Tree Based Classification  Advantages: Inexpensive to construct Extremely fast at classifying unknown records Easy to interpret for small-sized trees Accuracy is comparable to other classification techniques for many simple data sets

39. Bayesian Classification: Why?  Probabilistic learning: Calculate explicit probabilities for hypothesis, among the most practical approaches to certain types of learning problems  Incremental: Each training example can incrementally increase/decrease the probability that a hypothesis is correct. Prior knowledge can be combined with observed data.  Probabilistic prediction: Predict multiple hypotheses, weighted by their probabilities  Standard: Even when Bayesian methods are computationally intractable, they can provide a standard of optimal decision making against which other methods can be measured

40. Naïve Bayesian Classifier  A simplified assumption: attributes are conditionally independent:  Greatly reduces the computation cost, only count the class distribution. PVPP jjij i n CCvC (|)()(|)    1

41. Naïve Bayesian Classification (continued)  Naïve assumption: attribute independence P(x 1,…,x k |C) = P(x 1 |C)·…·P(x k |C)  If i-th attribute is categorical: P(x i |C) is estimated as the relative freq of samples having value x i as i-th attribute in class C  If i-th attribute is continuous: P(x i |C) is estimated thru a Gaussian density function  Computationally easy in both cases

42. Play-tennis example: estimating P(x i |C) outlook P(sunny|p) = 2/9P(sunny|n) = 3/5 P(overcast|p) = 4/9P(overcast|n) = 0 P(rain|p) = 3/9P(rain|n) = 2/5 temperature P(hot|p) = 2/9P(hot|n) = 2/5 P(mild|p) = 4/9P(mild|n) = 2/5 P(cool|p) = 3/9P(cool|n) = 1/5 humidity P(high|p) = 3/9P(high|n) = 4/5 P(normal|p) = 6/9P(normal|n) = 2/5 windy P(true|p) = 3/9P(true|n) = 3/5 P(false|p) = 6/9P(false|n) = 2/5 P(p) = 9/14 P(n) = 5/14 2 classes – p (play), n (don’t play)

43. Play-tennis example: classifying X  An unseen sample X =  P(X|p)·P(p) = P(rain|p)·P(hot|p)·P(high|p)·P(false|p)·P(p) = 3/9·2/9·3/9·6/9·9/14 = 0.010582  P(X|n)·P(n) = P(rain|n)·P(hot|n)·P(high|n)·P(false|n)·P(n) = 2/5·2/5·4/5·2/5·5/14 = 0.018286  Sample X is classified in class n (don ’ t play)