1. START OF DAY 6 Reading: Chap. 8

2. Group Project Progress Report

3. 3 Minute Synopsis What have you done? Where are you going? Thoughts on how you are going to get there

4. Model Combination

5. Prophetic Warning Now it is not common that the voice of the people desireth anything contrary to that which is right; but it is common for the lesser part of the people to desire that which is not right; therefore this shall ye observe and make it your law--to do your business by the voice of the people. (Mosiah 29:26) What is the point? One person may get it wrong Many less likely so

6. Following the Prophet Learning algorithms have different biases – They probably do not make the same mistakes – If one makes a mistake, the others may not Solution: model combination – Exploit variation in data Bagging, Boosting – Exploit variation in algorithms Ensemble, Stacking, Cascade Generalization, Cascading, Delegating, Arbitrating Sometimes called metalearning

7. Bagging (I)

8. Bagging (II)

9. Boosting (I)

10. Boosting (II)

11. Ensemble (I)

12. Ensemble (II) Key issue: diversity

13. Classifier Output Distance Measures difference in behavior Accuracy problematic – A and are both 50% accurate on T – Appear the same, yet A misses what B gets right, and vice versa! COD = ratio of number of disagreements between A and B to the total number of instances – COD(A,B)=1 (maximum)

14. Stacking (I)

15. Stacking (II)

16. Cascade Generalization (I)

17. Cascade Generalization (II) 2-step

18. Cascade Generalization (III) n-step

19. Cascading (I)

20. Cascading (II)

21. Delegating (I)

22. Delegating (II)

23. Arbitrating (I)

24. Arbitrating (II)

25. END OF DAY 6 Homework: Classification Model Evaluation