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Feature Engineering Studio

Feature Engineering Studio. October 14, 2013. Iterative Feature Refinement. Who here. Used the Excel Equation Solver Did not use the Excel Equation Solver. Excel Equation Solver Users. Sort yourself by the town you were born in (in Roman letters). Excel Equation Solver Users.

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Feature Engineering Studio

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  1. Feature Engineering Studio October 14, 2013

  2. Iterative Feature Refinement

  3. Who here • Used the Excel Equation Solver • Did not use the Excel Equation Solver

  4. Excel Equation Solver Users • Sort yourself by the town you were born in (in Roman letters)

  5. Excel Equation Solver Users • Pick one feature • What feature did you improve? • What parameter did you adjust? • What was the original and final value? • How big an improvement did you obtain? • Did this process change the meaning of the feature?

  6. Everyone Else • Sort yourself by the town you were born in (in Roman letters)

  7. Everyone Else • Pick one feature • What feature did you improve? • What parameter did you adjust? • What values did you try? • How big an improvement did you obtain? • Did this process change the meaning of the feature?

  8. Comments? Questions? Thoughts?

  9. Question • Is the excel equation solver likely to change the meaning of the feature more than hand processes?

  10. Question • Is it a good thing or a bad thing, when your feature changes meaning due to refinement?

  11. Feature Parameter Space • I need a volunteer who had a final best feature that was quite different from their original feature

  12. One interesting exercise • I need a volunteer who had a final best feature that was quite different from their original feature • Please bring up your laptop or a flash drive with your data set

  13. Making… • A line graph • X axis – parameter value • Y axis – model goodness

  14. Another volunteer? • Would anyone else like to look at their feature this way? • Multiple volunteers are welcome

  15. What does it mean?

  16. Questions? Comments? Thoughts?

  17. Assignment 6 • Feature Adaptation“This One’s For Nikolai IvonavichLobachevsky”

  18. Nikolai IvonovichLobachevsky(by Tom Lehrer) “I will never forget the day I first met the great Lobachevsky.In one word he told me the secret of success in mathematics:”

  19. Nikolai IvonovichLobachevsky(by Tom Lehrer) “I will never forget the day I first met the great Lobachevsky.In one word he told me the secret of success in mathematics:Plagiarize!”

  20. Nikolai IvonovichLobachevsky(by Tom Lehrer) “I will never forget the day I first met the great Lobachevsky.In one word he told me the secret of success in mathematics:Plagiarize!” “Only be sure to always call it – please – research.”

  21. To be clear… • Plagiarism: bad

  22. To be clear… • Plagiarism: bad • Borrowing ideas (and citing them): good

  23. To be clear… • Plagiarism: bad • Borrowing ideas (and citing them): good • We all clear?

  24. Assignment 6 • You need to find a previous paper that uses one or more features that can be potentially translated to your current analysis • Find the paper • Try at least one feature in your own data set

  25. Assignment 6 • You need to create a 5-minute presentation • Time yourself to make sure it only runs 5 minutes • To be presented in class next Monday

  26. Assignment 6 • This presentation should discuss • The paper you drew inspiration from • Give a full citation and show us pictures of as many authors as you can find • The construct being predicted in this paper • The context/data set in this paper • The feature you decided to adapt • The feature you ended up creating • Differences between the original paper’s feature and your feature • The goodness of your feature in your data set

  27. What if you can’t find a paper?

  28. What if you can’t find a paper? • You can find a paper

  29. What if you can’t find a paper? • You can find a paper • Try google scholar

  30. What if you can’t find a paper? • You can find a paper • Try google scholar • Email me – but only after you have spent at least 2 hours searching the web

  31. Questions? Comments?

  32. Upcoming Classes • 10/16 No special session today • 10/21 Feature Adaptation • Assignment 6 due • 10/23 Special Session on Building Prediction Models

  33. Upcoming Classes • 10/28 Feature Reuse • 10/30 No special session today

  34. Thank you!

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