1. Categorical data

2. Decision Tree Classification

3. Which feature to split on? Try to classify as many as possible with each split (This is a good split)

4. Which feature to split on? This is a bad split – no classifications obtained

5. Improving a good split

6. Decision Tree Algorithm Framework If you have positive and negative examples, use a splitting criterion to decide on best attribute to split Each child is a new decision tree – call the algorithm again with the parent feature removed If all data points in child node are same class, classify node as that class If no attributes left, classify by majority rule If no data points left, no such example seen: classify as majority class from entire dataset

7. Splitting Criterion ID3 Algorithm Some information theory Blackboard

8. Issues on training and test sets Do you know the correct classification for the test set? If you do, why not include it in the training set to get a better classifier? If you don’t, how can you measure the performance of your classifier?

9. Cross Validation Tenfold cross-validation Ten iterations Pull a different tenth of the dataset out each time to act as a test set Train on the remaining training set Measure performance on the test set Leave one out cross-validation Similar, but leave only one point out each time, then count correct vs. incorrect

10. Noise and Overfitting Can we always obtain a decision tree that is consistent with the data? Do we always want a decision tree that is consistent with the data? Example: Predict Carleton students who become CEOs Features: state/country of origin, GPA letter, major, age, high school GPA, junior high GPA,... What happens with only a few features? What happens with many features?

11. Overfitting Fitting a classifier “too closely” to the data finding patterns that aren’t really there Prevented in decision trees by pruning When building trees, stop recursion on irrelevant attributes Do statistical tests at node to determine if should continue or not

12. Examples of decision trees using Weka

13. Preventing overfitting by cross validation Another technique to prevent overfitting (is this valid)? Keep on recursing on decision tree as long as you continue to get improved accuracy on the test set

14. Review of how to decide on which attribute to split Dataset has two classes, P and N Relationship between information and randomness The more random a dataset is (points in P and N), the more information is provided by the message “Your point is in class P (or N).” The less random a dataset is, the less information is provided by the message “Your point is in class P (or N).” Information of message = Randomness of dataset =

15. How much randomness in split?

17. Which split is better? Patrons split Randomness = 0.4591 Type split Randomness = 1 Patrons has less randomness, so it is a better split Randomness is often referred to as entropy (similarities with thermodynamics)

18. Learning Logical Descriptions Hypothesis

19. Learning Logical Descriptions Goal is to learn a logical hypothesis consistent with the data Example of hypothesis consistent with X1: Is this consistent with X2? X2 is a false negative for hypothesis if hypothesis says negative, but should be positive X2 is a false positive for hypothesis if hypothesis says positive, but should be negative

20. Current-best-hypothesis search Start with an initial hypothesis and adjust it as you see examples Example: based on X1, arbitrarily start with X2 should be -, but H1 says +. H1 is not restrictive enough, specialize it: X3 should be +, but H2 says -. H2 is too restrictive, generalize:

21. Current-best-hypothesis search X4 should be +, H3 says -. Must generalize: What if you end up with an inconsistent hypothesis that you cannot modify to make work? Backup search and try a different route Tree on blackboard

22. Neural Networks Moving on to Chapter 19, neural networks