1. CS B351: D ECISION T REES

2. A GENDA Decision trees Learning curves Combatting overfitting

3. C LASSIFICATION T ASKS  Supervised learning setting  The target function f(x) takes on values True and False  A example is positive if f is True, else it is negative  The set X of all possible examples is the example set  The training set is a subset of X a small one!

4. L OGICAL C LASSIFICATION D ATASET Here, examples (x, f(x)) take on discrete values

5. L OGICAL C LASSIFICATION D ATASET Here, examples (x, f(x)) take on discrete values Concept Note that the training set does not say whether an observable predicate is pertinent or not

6. L OGICAL C LASSIFICATION T ASK  Find a representation of CONCEPT in the form: CONCEPT(x)  S(A,B, …) where S(A,B,…) is a sentence built with the observable attributes, e.g.: CONCEPT(x)  A(x)  (  B(x) v C(x))

7. P REDICATE AS A D ECISION T REE The predicate CONCEPT(x)  A(x)  (  B(x) v C(x)) can be represented by the following decision tree: A? B? C? True FalseTrue False Example: A mushroom is poisonous iff it is yellow and small, or yellow, big and spotted x is a mushroom CONCEPT = POISONOUS A = YELLOW B = BIG C = SPOTTED

8. P REDICATE AS A D ECISION T REE The predicate CONCEPT(x)  A(x)  (  B(x) v C(x)) can be represented by the following decision tree: A? B? C? True FalseTrue False Example: A mushroom is poisonous iff it is yellow and small, or yellow, big and spotted x is a mushroom CONCEPT = POISONOUS A = YELLOW B = BIG C = SPOTTED D = FUNNEL-CAP E = BULKY

9. T RAINING S ET Ex. #ABCDECONCEPT 1False TrueFalseTrueFalse 2 TrueFalse 3 True False 4 TrueFalse 5 True False 6TrueFalseTrueFalse True 7 False TrueFalseTrue 8 FalseTrueFalseTrue 9 FalseTrue 10True 11True False 12True False TrueFalse 13TrueFalseTrue

10. P OSSIBLE D ECISION T REE D CE B E AA A T F F FF F T T T TT

11. D CE B E AA A T F F FF F T T T TT CONCEPT  (D  (  E v A)) v (  D  (C  (B v (  B  ((E  A) v (  E  A)))))) A? B? C? True FalseTrue False CONCEPT  A  (  B v C)

12. P OSSIBLE D ECISION T REE D CE B E AA A T F F FF F T T T TT A? B? C? True FalseTrue False CONCEPT  A  (  B v C) KIS bias  Build smallest decision tree Computationally intractable problem  greedy algorithm CONCEPT  (D  (  E v A)) v (  D  (C  (B v (  B  ((E  A) v (  E  A))))))

13. G ETTING S TARTED : T OP -D OWN I NDUCTION OF D ECISION T REE Ex. #ABCDECONCEPT 1False TrueFalseTrueFalse 2 TrueFalse 3 True False 4 TrueFalse 5 True False 6TrueFalseTrueFalse True 7 False TrueFalseTrue 8 FalseTrueFalseTrue 9 FalseTrue 10True 11True False 12True False TrueFalse 13TrueFalseTrue True: 6, 7, 8, 9, 10,13 False: 1, 2, 3, 4, 5, 11, 12 The distribution of training set is:

14. G ETTING S TARTED : T OP -D OWN I NDUCTION OF D ECISION T REE True: 6, 7, 8, 9, 10,13 False: 1, 2, 3, 4, 5, 11, 12 The distribution of training set is: Without testing any observable predicate, we could report that CONCEPT is False (majority rule) with an estimated probability of error P(E) = 6/13 Assuming that we will only include one observable predicate in the decision tree, which predicate should we test to minimize the probability of error (i.e., the # of misclassified examples in the training set)?  Greedy algorithm

15. A SSUME I T ’ S A A True: False: 6, 7, 8, 9, 10, 13 11, 12 1, 2, 3, 4, 5 T F If we test only A, we will report that CONCEPT is True if A is True (majority rule) and False otherwise  The number of misclassified examples from the training set is 2

16. A SSUME I T ’ S B B True: False: 9, 10 2, 3, 11, 12 1, 4, 5 T F If we test only B, we will report that CONCEPT is False if B is True and True otherwise  The number of misclassified examples from the training set is 5 6, 7, 8, 13

17. A SSUME I T ’ S C C True: False: 6, 8, 9, 10, 13 1, 3, 4 1, 5, 11, 12 T F If we test only C, we will report that CONCEPT is True if C is True and False otherwise  The number of misclassified examples from the training set is 4 7

18. A SSUME I T ’ S D D T F If we test only D, we will report that CONCEPT is True if D is True and False otherwise  The number of misclassified examples from the training set is 5 True: False: 7, 10, 13 3, 5 1, 2, 4, 11, 12 6, 8, 9

19. A SSUME I T ’ S E E True: False: 8, 9, 10, 13 1, 3, 5, 12 2, 4, 11 T F If we test only E we will report that CONCEPT is False, independent of the outcome  The number of misclassified examples from the training set is 6 6, 7

20. A SSUME I T ’ S E E True: False: 8, 9, 10, 13 1, 3, 5, 12 2, 4, 11 T F If we test only E we will report that CONCEPT is False, independent of the outcome  The number of misclassified examples from the training set is 6 6, 7 So, the best predicate to test is A

21. C HOICE OF S ECOND P REDICATE A T F C True: False: 6, 8, 9, 10, 13 11, 12 7 T F False  The number of misclassified examples from the training set is 1

22. C HOICE OF T HIRD P REDICATE C T F B True: False: 11,12 7 T F A T F False True

23. F INAL T REE A C True B False CONCEPT  A  (C v  B) CONCEPT  A  (  B v C) A? B? C? True False True False

24. T OP -D OWN I NDUCTION OF A DT DTL( , Predicates) 1. If all examples in  are positive then return True 2. If all examples in  are negative then return False 3. If Predicates is empty then return failure 4. A  error-minimizing predicate in Predicates 5. Return the tree whose: - root is A, - left branch is DTL(  +A,Predicates-A), - right branch is DTL(  -A,Predicates-A) A C True B False Subset of examples that satisfy A

25. T OP -D OWN I NDUCTION OF A DT DTL( , Predicates) 1. If all examples in  are positive then return True 2. If all examples in  are negative then return False 3. If Predicates is empty then return failure 4. A  error-minimizing predicate in Predicates 5. Return the tree whose: - root is A, - left branch is DTL(  +A,Predicates-A), - right branch is DTL(  -A,Predicates-A) A C True B False Noise in training set! May return majority rule, instead of failure

26. T OP -D OWN I NDUCTION OF A DT DTL( , Predicates) 1. If all examples in  are positive then return True 2. If all examples in  are negative then return False 3. If Predicates is empty then return majority rule 4. A  error-minimizing predicate in Predicates 5. Return the tree whose: - root is A, - left branch is DTL(  +A,Predicates-A), - right branch is DTL(  -A,Predicates-A) A C True B False

27. C OMMENTS Widely used algorithm Easy to extend to k-class classification Greedy Robust to noise (incorrect examples) Not incremental

28. H UMAN -R EADABILITY DTs also have the advantage of being easily understood by humans Legal requirement in many areas Loans & mortgages Health insurance Welfare

29. L EARNABLE C ONCEPTS Some simple concepts cannot be represented compactly in DTs Parity(x) = X 1 xor X 2 xor … xor X n Majority(x) = 1 if most of X i ’s are 1, 0 otherwise Exponential size in # of attributes Need exponential # of examples to learn exactly The ease of learning is dependent on shrewdly (or luckily) chosen attributes that correlate with CONCEPT

30. P ERFORMANCE I SSUES Assessing performance: Training set and test set Learning curve size of training set % correct on test set 100 Typical learning curve

31. P ERFORMANCE I SSUES Assessing performance: Training set and test set Learning curve size of training set % correct on test set 100 Typical learning curve Some concepts are unrealizable within a machine’s capacity

32. P ERFORMANCE I SSUES Assessing performance: Training set and test set Learning curve Overfitting Risk of using irrelevant observable predicates to generate an hypothesis that agrees with all examples in the training set size of training set % correct on test set 100 Typical learning curve

33. P ERFORMANCE I SSUES Assessing performance: Training set and test set Learning curve Overfitting Tree pruning Risk of using irrelevant observable predicates to generate an hypothesis that agrees with all examples in the training set Terminate recursion when # errors (or information gain) is small

34. P ERFORMANCE I SSUES Assessing performance: Training set and test set Learning curve Overfitting Tree pruning Terminate recursion when # errors (or information gain) is small Risk of using irrelevant observable predicates to generate an hypothesis that agrees with all examples in the training set The resulting decision tree + majority rule may not classify correctly all examples in the training set

35. S TATISTICAL M ETHODS FOR A DDRESSING O VERFITTING / N OISE There may be few training examples that match the path leading to a deep node in the decision tree More susceptible to choosing irrelevant/incorrect attributes when sample is small Idea: Make a statistical estimate of predictive power (which increases with larger samples) Prune branches with low predictive power Chi-squared pruning

36. T OP - DOWN DT PRUNING Consider an inner node X that by itself (majority rule) predicts p examples correctly and n examples incorrectly At k leaf nodes, number of correct/incorrect examples are p 1 /n 1,…,p k /n k Chi-squared statistical significance test: Null hypothesis: example labels randomly chosen with distribution p/(p+n) (X is irrelevant) Alternate hypothesis: examples not randomly chosen (X is relevant) Prune X if testing X is not statistically significant

37. C HI -S QUARED TEST Let Z =  i (p i – p i ’) 2 /p i ’ + (n i – n i ’) 2 /n i ’ Where p i ’ = p i (p i +n i )/(p+n), n i ’ = n i (p i +n i )/(p+n) are the expected number of true/false examples at leaf node i if the null hypothesis holds Z is a statistic that is approximately drawn from the chi-squared distribution with k degrees of freedom Look up p-Value of Z from a table, prune if p- Value >  for some  (usually ~.05)

38. P ERFORMANCE I SSUES Assessing performance: Training set and test set Learning curve Overfitting Tree pruning Incorrect examples Missing data Multi-valued and continuous attributes

39. M ULTI -V ALUED A TTRIBUTES Simple change: consider splits on all values A can take on Caveat: the more values A can take on, the more important it may appear to be, even if it is irrelevant More values => dataset split into smaller example sets when picking attributes Smaller example sets => more likely to fit well to spurious noise

40. C ONTINUOUS A TTRIBUTES Continuous attributes can be converted into logical ones via thresholds X => X<a When considering splitting on X, pick the threshold a to minimize # of errors / entropy 7765654543454567

41. D ECISION B OUNDARIES With continuous attributes, a decision boundary is the surface in example space that splits positive from negative examples x1>=20 x2 x1 TF FT

42. D ECISION B OUNDARIES With continuous attributes, a decision boundary is the surface in example space that splits positive from negative examples x1>=20 x2 x1 F F x2>=10 T F F T

43. D ECISION B OUNDARIES With continuous attributes, a decision boundary is the surface in example space that splits positive from negative examples x1>=20 x2 x1 F x2>=10 T F F T x2>=15 TF T

44. D ECISION B OUNDARIES With continuous attributes, a decision boundary is the surface in example space that splits positive from negative examples

45. E XERCISE With 2 attributes, what kinds of decision boundaries can be achieved by a decision tree with arbitrary splitting threshold and maximum depth: 1? 2? 3? Describe the appearance and the complexity of these decision boundaries

46. R EADING Next class: Neural networks & function learning R&N 18.6-7