Statistical LearningIntroduction:

Data Mining Process and Modeling Examples

Visualization example: Fraud by customer type

0

10

20

30

40

50

60

Type A Type B Type C

%

Legitimate (n=5000)

Fraud (n=200)

We can see associations between customer type and fraudulent behavior.

Are they legitimate? Data leakage?

Our goal is to build model to predict fraud in advance

Targeting,Sales force

mgmt.

Business problem definition

Wallet / opportunity estimation

Modeling problem definition

Quantile est.,Latent

variable est.

Statistical problem definition

Quantile est.,Graphical

model

Modeling methodology design

Programming,Simulation,IBM Wallets

Model generation & validation

OnTarget,MAP

Implementation & application development

Project evolution and relevance to our course

Outside scope

Keep in mind

This is our domain!

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

ESL Chap1 - Introduction

• Identify the risk factors for prostate cancer (lpsa), based on clinical and demographic variables.

• Classify a recorded phoneme, based on a log-periodogram.

A restricted model (red) does much better than an unrestricted one (jumpy black)

• Customize an email spam detection system.

X = which words appear and how muchY = Spam or not?

• Identify the numbers in a handwritten zip code, from a digitized image

X = color of each pixelY = which digit is it?

• Classify a tissue sample into one of several cancer classes, based on a gene expression profile.

X = expression levels of genesY = which cancer?

• 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

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 45878 contestants on 37660 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…

4 5 1

3

2

4

All movies (80K)

All

use

rs (

6.8

M)

NETFLIXCompetition

Data

17KSelection unclear

480 KAt least 20Ratings by end 2005

100 M ratings

Data Overview: NETFLIX Internet Movie Data Base

Fields

Title

Year

Actors

Awards

Revenue

…

17K

mo

vie

s

Training Data

Movie Arrival

1998 Time 2005

User Arrival

4 5 ?

3

2

?

QualifierDataset

3M

NETFLIX data generation process

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 will hopefully 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