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Problem Proposal Day: Presentations and Data Familiarization in Feature Engineering

Welcome to Problem Proposal Day! Presenters will have 3 minutes to discuss their dataset, prediction variables, and the significance of their work. The audience is encouraged to engage with respectful questions and suggestions. Criteria for evaluation emphasize the importance of the problem, the availability of ground truth measures, and sufficient rich data for meaningful feature extraction. Upcoming classes will focus on feature distillation, prediction models, and hands-on sessions with tools like Excel and RapidMiner. Let's collaboratively enhance our problem proposals!

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Problem Proposal Day: Presentations and Data Familiarization in Feature Engineering

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Presentation Transcript

  1. Feature Engineering Studio September 9, 2013

  2. Welcome to Problem Proposal Day • Rules for Presenters • Rules for the Rest of the Class

  3. Rules for Presenters • Talk for 3 minutes on: • Data set • What variable will you predict? • What kind of variables will you use to predict it? • Why is this worth doing? • Remember to send me your slides (if any)

  4. Rules for Audience • After the presentation • Ask quick questions • Give quick suggestions

  5. Criteria • Everyone • Is the problem genuinely important? (usable or publishable) • Is there a good measure of ground truth? • Only if you know what you’re talking about • Is there rich enough data to distill meaningful features? • Is there enough data to be able to take advantage of data mining?

  6. Rules for Audience • Be polite! • No interrupting • No rambling • No being mean

  7. First Step • Get into the right collaborative spirit • You are officially encouraged (though not required)to sing along • http://www.youtube.com/watch?v=pd_5-2kCzfs • 0:25

  8. Presentations • Alphabetical Order Based on Last Name • Tie-Breaker: First Name

  9. For next week • Think about how to improve your problem proposal • Rewrite your problem proposal based on the feedback you got today • Then email it to me for further feedback and a “thumbs-up” before the next class

  10. Assignment 2 • Data Familiarization“Mucking Around” • Get your data set • Open it in Excel • Look at your ground truth label (if you have one) • Look at other key variables • What does each variable mean semantically? • If numerical, what are its max, min, average, stdev? Create histograms of key variables. • If categorical, what is the distribution of each value?

  11. Assignment 2 • Data Familiarization“Mucking Around” • Write a brief report for me • You don’t need to prepare a presentation • But be ready to discuss what you learn about your data

  12. What if you don’t have data yet? • Get your data • If you can’t get your data before class, email me at least 48 hours before class and I’ll send you a practice data set

  13. How to compute in Excel • If numerical, what are its max, min, average, stdev? • If categorical, what is the distribution of each value? • Using Class2Data

  14. How to do a histogram in Excel • Using Class2Data

  15. Next Class • 9/23 Feature distillation in Excel (Asgn.2 due) • Do the assignment • Read the readings

  16. Upcoming Classes • 9/23 Feature distillation in Excel (Asgn.2 due) • 9/25 Special session on prediction models • Come to this if you don’t know why student-level cross-validation is important, or if you don’t know what J48 is • 9/30 Advanced feature distillation in Excel (Asgn. 3 due) • 10/2 Special session on RapidMiner • Come to this if you’ve never built a classifier or regressor in RapidMiner (or a similar tool) • Statistical significance tests using linear regression don’t count…

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