Feature Engineering Studio February 2, 2015

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? And please 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

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 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 (or another tool you prefer) 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, in class

What if you don’t have data yet? 1.Get your data

What if you don’t have data yet? 1.Get your data 2.If you don’t have your data yet, me at least 48 hours before the assignment is due 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 Session 2/4 Lab Session: Using RapidMiner – If you don’t know how to build a prediction model in RapidMiner, you should attend this session – If you do know how to build a prediction model in RapidMiner, you don’t have to attend

Next Class After That 2/16 Data Cleaning (Asgn.2 due) – Do the assignment – Read the readings

Note 2/9 No class 2/11 No class

Questions? Comments? Concerns?