1. Decision Trees

2. What is a decision tree? Input = assignment of values for given attributes –Discrete (often Boolean) or continuous Output = predicated value –Discrete - classification –Continuous - regression Structure: –Internal node - tests one attribute –Leaf node - output value (or linear function of attributes)

3. Example Attributes Alternate - Boolean Bar - Boolean Fri/Sat - Boolean Hungry - Boolean Patrons - {None, Some, Full} Price - {$, $$, $$$} Raining - Boolean Reservation - Boolean Type - {French, Italian, Thai, burger} WaitEstimate - {0-10min, 10-30, 30-60, >60}

4. Decision Tree for WillWait

5. Decision Tree Properties Propositional: attribute-value pairs Not useful for relationships between objects Universal: Space of possible decision trees includes every Boolean function Not efficient for all functions (e.g., parity, majority) Good for disjunctive functions

6. Decision Tree Learning From a training set, construct a tree

7. Methods for Constructing Decision Trees One path for each example in training set –Not robust, little predictive value Rather look for “small” tree –Applying Ockham’s Razor ID3: simple non-backtracking search through space of decision trees

8. Algorithm

9. Choose-Attribute Greedy algorithm - use attribute that gives the greatest immediate information gain

10. Information/Entropy Information provided by knowing an answer: –Possible answers v i with probability P(v i ) –I({P(v i )}) =  i -P(v i ) log 2 P(v i ) –I({0.5, 0.5}) = -0.5*(-1)-0.5*(-1) = 1 –I({0.01,0.99}) = -0.01*(-6.6) -0.99*(-0.014) = 0.08 Estimate probability from set of examples –Example: 6 yes, 6 no, estimate P(yes)=P(no)=0.5 1 bit required –After testing an attribute, take weighted sum of information required for subsets of the examples

11. After Testing “Type” 2/12*I(1/2,1/2) + 2/12*I(1/2,1/2) + 4/12*I(1/2,1/2) + 4/12*I(1/2,1/2) = 1

12. After Testing “Patrons” 2/12*I(0,1) + 4/12*I(1,0) + 6/12*I(2/6,4/6) = 0.46

13. Repeat adding tests Note: induced tree not the same as “true” function Best we can do given the examples

14. Overfitting Algorithm can find meaningless regularity E.g., use date & time to predict roll of die Approaches to fixing: –Stop the tree from growing too far –Allow the tree to overfit, then prune

15.  2 - Pruning Is a split irrelevant? –Information gain close to zero - how close? –Assume no underlying pattern (null hypothesis) –If statistical analysis shows <5% probability that null hypothesis is correct, then assume attribute is relevant Pruning provides noise tolerance

16. Rule Post-Pruning Convert tree into a set of rules (one per path) Prune preconditions (generalize) each rule if it improves accuracy Rules may now overlap Consider rules in order of accuracy when doing classification

17. Continuous-valued attributes Split based on some threshhold –X =97 Many possible split points

18. Multi-valued attributes Information gain of attribute with many values may be huge (e.g., Date) Rather than absolute info gain use ratio of gain to SplitInformation -

19. Continuous-valued outputs Leaf is a linear function of attributes, not value Regression Tree

20. Missing Attribute Values Attribute value in example may not be known –Assign most common value amongst comparable examples –Split into fractional examples based of observed distribution of values

21. Credits Diagrams from “Artificial Intelligence - A Modern Approach’’ by Russell and Norvig