1. Statistical Learning Introduction: Modeling Examples

2. Our goal is to build model to predict fraud in advance
We can see associations between customer type and fraudulent behavior.
Are they legitimate? Data leakage?

3. Predict whether someone will have a heart attack on the basis of demographic, diet and clinical measurements

4. ESL Chap1 - Introduction
Model test results for prostate cancer (lpsa), based on actual cancer volume (lcavol) and other clinical and demographic variables.
ESL Chap1 - Introduction

5. Classify a recorded phoneme, based on a log-periodogram.
A restricted model (red) does much better than an unrestricted one (jumpy black)

6. Customize an email spam detection system.
X = which words appear and how much
Y = Spam or not?

7. Identify the numbers in a handwritten zip code, from a digitized image
X = color of each pixel Y = which digit is it?

8. Classify a tissue sample into one of several cancer classes, based on a gene expression profile.
X = expression levels of genes Y = which cancer?

9. Classify the pixels in a LANDSAT image, according to usage: Y = {red soil, cotton, vegetation stubble, mixture, gray soil, damp gray soil, very damp gray soil} X = values of pixels in several wavelength bands

10. 80.000 DVD titles, 6.8 Million users
2000 online movies
1400 employers

11. October 2006 Announcement of the NETFLIX Competition
USAToday headline:
“Netflix offers $1 million prize for better movie recommendations”
Details:
Beat NETFLIX current recommender model ‘Cinematch’ by 10% based on absolute rating error prior to 2011
$50K for the annual progress price (relative to baseline)
Data contains a subset of 100 million movie ratings from NETFLIX including 480,189 users and 17,770 movies
Performance is evaluated on holdout movies-users pairs
NETFLIX competition has attracted contestants on teams from 180 different countries
Tens of thousands of valid submissions from thousands of teams
Conclusion: in 2009, an international team attained the goal and won the prize! More later…
Public in 2002 (at around $20)

12. Data Overview: NETFLIX Internet Movie Data Base
All movies (80K)
17K
Selection
unclear
All users (6.8 M)
NETFLIX
Competition
Data
480 K
At least 20
Ratings by
end 2005
Fields
Title
Year
Actors
Awards
Revenue …
100 M ratings 4 5 1 3 2

13. NETFLIX data generation process
User Arrival
Movie Arrival
17K movies
Training Data
Time 4 5 ? 3 2
Qualifier
Dataset 3M

14. Netflix and us
We will have a modeling challenge in our course which will use the Netflix data. The winners will get a grade boost!
The $1M was won in 2009 by a collaboration of several leading teams
The strongest team, which won both yearly $50K prizes, was founded at AT&T, with an Israeli participant (Yehuda Koren)
Yehuda was one of the major driving forces on the final winning team
He is now back in Israel, and may come give us a talk!
While I was at IBM Research, our team won a related competition in KDD-Cup 2007 (same data, more “standard” modeling tasks)
We may have a “case study” lecture on that as well

15. Project evolution and relevance to our course
Business problem definition
Modeling problem definition
Statistical problem definition
Modeling methodology design
Targeting,
Sales force mgmt.
Wallet / opportunity estimation
Quantile est., Latent variable est.
Quantile est., Graphical model
Outside scope
Model generation & validation
Implementation & application development
Keep in mind
Programming, Simulation, IBM Wallets
OnTarget, MAP
This is our domain!