1. Classification Today: Basic Problem Decision Trees

2. Classification Problem Given a database D={t 1,t 2,…,t n } and a set of classes C={C 1,…,C m }, the Classification Problem is to define a mapping f:D  C where each t i is assigned to one class. Actually divides D into equivalence classes. Prediction is similar, but may be viewed as having infinite number of classes.

3. Classification Ex: Grading If x >= 90 then grade =A. If 80<=x<90 then grade =B. If 70<=x<80 then grade =C. If 60<=x<70 then grade =D. If x<50 then grade =F. >=90<90 x >=80<80 x >=70<70 x F B A >=60<50 x C D

4. Classification Techniques Approach: 1.Create specific model by evaluating training data (or using domain experts’ knowledge). 2.Apply model developed to new data. Classes must be predefined Most common techniques use DTs, or are based on distances or statistical methods.

5. Defining Classes Partitioning Based Distance Based

6. Issues in Classification Missing Data –Ignore –Replace with assumed value Measuring Performance –Classification accuracy on test data –Confusion matrix –OC Curve

7. Height Example Data

8. Classification Performance True Positive True NegativeFalse Positive False Negative

9. Confusion Matrix Example Using height data example with Output1 correct and Output2 actual assignment

10. Operating Characteristic Curve

11. Classification Using Decision Trees Partitioning based: Divide search space into rectangular regions. Tuple placed into class based on the region within which it falls. DT approaches differ in how the tree is built: DT Induction Internal nodes associated with attribute and arcs with values for that attribute. Algorithms: ID3, C4.5, CART

12. Decision Tree Given: –D = {t 1, …, t n } where t i = –Database schema contains {A 1, A 2, …, A h } –Classes C={C 1, …., C m } Decision or Classification Tree is a tree associated with D such that –Each internal node is labeled with attribute, A i –Each arc is labeled with predicate which can be applied to attribute at parent –Each leaf node is labeled with a class, C j

13. DT Induction

14. DT Splits Area Gender Height M F

15. Comparing DTs Balanced Deep

16. DT Issues Choosing Splitting Attributes Ordering of Splitting Attributes Splits Tree Structure Stopping Criteria Training Data Pruning

17. Information/Entropy Given probabilitites p 1, p 2,.., p s whose sum is 1, Entropy is defined as: Entropy measures the amount of randomness or surprise or uncertainty. Goal in classification – no surprise – entropy = 0

18. ID3 Creates tree using information theory concepts and tries to reduce expected number of comparison.. ID3 chooses split attribute with the highest information gain:

19. ID3 Example (Output1) Starting state entropy: 4/15 log(15/4) + 8/15 log(15/8) + 3/15 log(15/3) = 0.4384 Gain using gender: –Female: 3/9 log(9/3)+6/9 log(9/6)=0.2764 –Male: 1/6 (log 6/1) + 2/6 log(6/2) + 3/6 log(6/3) = 0.4392 –Weighted sum: (9/15)(0.2764) + (6/15)(0.4392) = 0.34152 –Gain: 0.4384 – 0.34152 = 0.09688 Gain using height: 0.4384 – (2/15)(0.301) = 0.3983 Choose height as first splitting attribute

20. C4.5 ID3 favors attributes with large number of divisions Improved version of ID3: –Missing Data –Continuous Data –Pruning –Rules –GainRatio:

21. CART Create Binary Tree Uses entropy Formula to choose split point, s, for node t: P L,P R probability that a tuple in the training set will be on the left or right side of the tree.

22. CART Example At the start, there are six choices for split point (right branch on equality): –P(Gender)= 2(6/15)(9/15)(2/15 + 4/15 + 3/15)=0.224 –P(1.6) = 0 –P(1.7) = 2(2/15)(13/15)(0 + 8/15 + 3/15) = 0.169 –P(1.8) = 2(5/15)(10/15)(4/15 + 6/15 + 3/15) = 0.385 –P(1.9) = 2(9/15)(6/15)(4/15 + 2/15 + 3/15) = 0.256 –P(2.0) = 2(12/15)(3/15)(4/15 + 8/15 + 3/15) = 0.32 Split at 1.8

23. Problem to Work On: Training Dataset This follows an example from Quinlan’s ID3

24. Output: A Decision Tree for “buys_computer” age? overcast student?credit rating? noyes fair excellent <=30 >40 no yes 30..40

25. 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

26. Bayesian Theorem: Basics Let X be a data sample whose class label is unknown Let H be a hypothesis that X belongs to class C For classification problems, determine P(H/X): the probability that the hypothesis holds given the observed data sample X P(H): prior probability of hypothesis H (i.e. the initial probability before we observe any data, reflects the background knowledge) P(X): probability that sample data is observed P(X|H) : probability of observing the sample X, given that the hypothesis holds

27. Bayes Theorem (Recap) Given training data X, posteriori probability of a hypothesis H, P(H|X) follows the Bayes theorem MAP (maximum posteriori) hypothesis Practical difficulty: require initial knowledge of many probabilities, significant computational cost; insufficient data

28. Naïve Bayes Classifier A simplified assumption: attributes are conditionally independent: The product of occurrence of say 2 elements x 1 and x 2, given the current class is C, is the product of the probabilities of each element taken separately, given the same class P([y 1,y 2 ],C) = P(y 1,C) * P(y 2,C) No dependence relation between attributes Greatly reduces the computation cost, only count the class distribution. Once the probability P(X|C i ) is known, assign X to the class with maximum P(X|C i )*P(C i )

29. Training dataset Class: C1:buys_computer= ‘yes’ C2:buys_computer= ‘no’ Data sample X =(age<=30, Income=medium, Student=yes Credit_rating= Fair)

30. Naïve Bayesian Classifier: Example Compute P(X/Ci) for each class P(age=“<30” | buys_computer=“yes”) = 2/9=0.222 P(age=“<30” | buys_computer=“no”) = 3/5 =0.6 P(income=“medium” | buys_computer=“yes”)= 4/9 =0.444 P(income=“medium” | buys_computer=“no”) = 2/5 = 0.4 P(student=“yes” | buys_computer=“yes)= 6/9 =0.667 P(student=“yes” | buys_computer=“no”)= 1/5=0.2 P(credit_rating=“fair” | buys_computer=“yes”)=6/9=0.667 P(credit_rating=“fair” | buys_computer=“no”)=2/5=0.4 X=(age<=30,income =medium, student=yes,credit_rating=fair) P(X|Ci) : P(X|buys_computer=“yes”)= 0.222 x 0.444 x 0.667 x 0.0.667 =0.044 P(X|buys_computer=“no”)= 0.6 x 0.4 x 0.2 x 0.4 =0.019 Multiply by P(Ci)s and we can conclude that X belongs to class “buys_computer=yes”

31. Naïve Bayesian Classifier: Comments Advantages : –Easy to implement –Good results obtained in most of the cases Disadvantages –Assumption: class conditional independence, therefore loss of accuracy –Practically, dependencies exist among variables –E.g., hospitals: patients: Profile: age, family history etc Symptoms: fever, cough etc., Disease: lung cancer, diabetes etc –Dependencies among these cannot be modeled by Naïve Bayesian Classifier How to deal with these dependencies? –Bayesian Belief Networks

32. Classification Using Distance Place items in class to which they are “closest”. Must determine distance between an item and a class. Classes represented by –Centroid: Central value. –Medoid: Representative point. –Individual points Algorithm: KNN

33. K Nearest Neighbor (KNN): Training set includes classes. Examine K items near item to be classified. New item placed in class with the most number of close items. O(q) for each tuple to be classified. (Here q is the size of the training set.)

34. KNN

35. KNN Algorithm