1. Feature Engineering Studio October 7, 2013

2. Welcome to Bring Me a Rock Day 2

3. But first… Excel Equation Solver

4. What it requires Parameters Goodness metric (typically SSR)

5. Linear Regression Example Look at prior variables – And how model prediction is created from predictor Create SSR variable

6. Linear Regression Example Hand-iterate on variables

7. Linear Regression Example Excel equation solver

8. BKT Example Go through functions

9. BKT Example Excel equation solver

10. BKT Example Excel equation solver – Constrain P(G) to under 0.3

11. BKT Example Excel equation solver – Try different solver algorithms

12. Questions? Comments?

13. GoogleRefine (now OpenRefine)

14. Functionality to make it easy to regroup and transform data – Find similar names – Connect names – Bin numerical data – Mathematical transforms showing resultant graphs – Text transforms and column creation

15. GoogleRefine (now OpenRefine) Functionality for finding anomalies/outliers

16. GoogleRefine (now OpenRefine) Functionality for automatically repeating the same process on a new data set *Really* nice for cases where you complete a complex process and want to repeat it

17. GoogleRefine (now OpenRefine) Functionality for connecting your data set to web services to get additional relevant info

18. GoogleRefine (now OpenRefine) Can load in and export common but hard-to- work-with data types – JSON and XML

19. GoogleRefine (now OpenRefine) Some videos you should watch later http://www.youtube.com/watch?v=B70J_H_z AWM http://www.youtube.com/watch?v=cO8NVCs_ Ba0 http://www.youtube.com/watch?v=5tsyz3ibYz k

20. Questions? Comments?

21. Welcome to Bring Me a Rock Day 2

22. In birthdate order Each person should tell us about their favorite feature they created for Bring Me a Rock Day 2 Tell us what it was How you created it Your just-so story And was your just-so story correct

23. Next Tell us about anything cool you did in Excel or another program to create a feature

24. Too Hard? Were there any features that anyone kind of wanted to create, but it was too difficult? (or too much work?)

25. Better? Who here got better features (in terms of goodness metric) for Bring Me a Rock Day 2, than Bring Me a Rock Day 1?

26. Other Interesting Observations?

27. Assignment 5

28. Iterative Feature Refinement – Select three of the features you have created in previous assignments – These features should be “among the best” of the features you have previously created – For each of these three features, create at least five “close variants” of these features “time for last 3 actions” and “time for last 4 actions” are close variants “time for last 3 actions” and “total time between help requests and next action” are two separate features – Using the Excel Equation Solver is a substitute for creating five “close variants” – If you don’t use the excel equation solver As you create the close variants for each feature, don’t just make them all at once Make a variant Test whether it’s better than the previous variant (by goodness metric) – If it is, keep going in the same direction – If it isn’t, try doing the opposite or something else

29. Assignment 5 Write a report that discusses your process – I took feature N – I changed it from N to N* – The goodness changed from G to G* – Then I did…

30. Assignment 5 You don’t need to prepare a presentation But be ready to discuss your features in class

31. Next Classes 10/9 RapidMiner Practice Session – Bring your RapidMiner process to class with questions, on a laptop – We’ll learn together 10/14 Iterative Feature Refinement – Assignment 5 due

32. Upcoming Classes 10/16 No special session today 10/21 Feature Adaptation 10/23 Special Session on Building Prediction Models

33. Thank you!