Feature Engineering Studio

September 9, 2013

Welcome to Problem Proposal Day

• Rules for Presenters• Rules for the Rest of the Class

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)

Rules for Audience

• After the presentation– Ask quick questions– Give quick suggestions

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?

Rules for Audience

• Be polite!

• No interrupting• No rambling• No being mean

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

Presentations

• Alphabetical Order Based on Last Name– Tie-Breaker: First Name

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

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?

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

What if you don’t have data yet?

1. Get your data2. 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

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

How to do a histogram in Excel

• Using Class2Data

Next Class

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

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…