Predictive Analytics: Modeling the World. Richard D. De Veaux Professor of Statistics, Williams College January 28, 2005 OR/MS Seminar

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1 Predictive Analytics: Modeling the World Richard D. De Veaux Professor of Statistics, Williams College January 28, 2005 OR/MS Seminar

2 Getting to Know Your Customers 50 years ago this was easy Customer data base could fit in one person s head Retention of customers depended on ability to do so 2

3 21 st Century Data Bases Ability to anticipate customer s needs crucial for retention Even Sam Walton didn t know all his customer s preferences Amazon.com Earth s biggest selection $390,000 Diamond Necklace World s biggest book Yak Cheese from Tibet No one can do this without help Well, almost no one! 3

4 Direct Marketing Example Paralyzed Veterans of America KDD 1998 cup Mailing list of 3.5 million potential donors Lapsed donors Made their last donation to PVA 13 to 24 months prior to June ,000 (training and test sets) Who should get the current mailing? Cost effective strategy 4

5 Why is this Hard? Amount of Information 481 predictors 2 responses Cross tabs / OLAP How many combinations? What to focus on? Data Preparation This alone can be 60-95% of the effort Categorical vs. Quantitative 5

6 What s Hard? --Example 6

7 T-Code 7

8 So, what does it mean? T-Code Title 0 _ 1 6 DEAN 4 8 CORP ORAL LIC. 1 M R. 1 7 J UDGE 5 0 ELDER S A M ES SRS J UDGE & M RS. 5 6 M AYOR DA M R. & M RS. 1 8 M AJ OR LIEUTENANT & M RS S R. 2 M RS M AJ OR & M RS. 6 2 LORD S RA MESDAMES 19 SENATOR 63 CARDINAL 118 SRTA. 3 M IS S 2 0 GOVERNOR 6 4 FRIEND YOUR M AJ ES TY M IS S ES S ERGEANT & M RS. 6 5 FRIENDS HIS HIGHNES S 4 DR COLNEL & MRS. 68 ARCHDEACON 123 HER HIGHNESS 4002 DR. & MRS. 24 LIEUTENANT 69 CANON 124 COUNT DOCTORS 2 6 M ONSIGNOR 7 0 BIS HOP LADY 5 MADAME 27 REVEREND REVEREND & MRS. 126 PRINCE 6 S ERGEANT 2 8 M S. 7 3 PAS TOR P RINCES S 9 RABBI MSS. 75 ARCHBISHOP 128 CHIEF 1 0 P ROFES S OR 2 9 BIS HOP 8 5 SP ECIALIS T BARON P ROFES S OR & M RS. 3 1 AM BAS S ADOR 8 7 PRIVATE S HEIK P ROFES S ORS AM BAS S ADOR & M RS 8 9 SEAM AN P RINCE AND P RINCES S 1 1 ADM IRAL 3 3 CANTOR 9 0 AIRM AN YOUR IM PERIAL M AJ ES T ADM IRAL & M RS. 3 6 BROTHER 9 1 J US TICE M. ET M M E. 1 2 GENERAL 3 7 S IR 9 2 M R. J US TICE P ROF GENERAL & M RS. 3 8 COM M ODORE M. 1 3 COLONEL 4 0 FATHER M LLE COLONEL & MRS. 42 SISTER 104 CHANCELLOR 1 4 CAPTAIN 4 3 P RES IDENT REP RES ENTATIV E CAPTAIN & M RS. 4 4 M AS TER SECRETARY 1 5 COM M ANDER 4 6 M OTHER LT. GOVERNOR COM M ANDER & M RS. 4 7 CHAP LAIN 8

9 Results for PVA Data Set If entire list (100,000 donors) are mailed, net donation is $10,500 Using data mining techniques, this was increased 41.37% 9

10 KDD CUP 98 Results 10

11 KDD CUP 98 Results 2 11

12 Data Mining Is the nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data. --- Fayyad finding interesting structure (patterns, statistical models, relationships) in data bases.--- Fayyad, Chaduri and Bradley a knowledge discovery process of extracting previously unknown, actionable information from very large data bases --- Zornes a process that uses a variety of data analysis tools to discover patterns and relationships in data that may be used to make valid predictions. ---Edelstein 12

13 Data Mining Is 13

14 Case Study I Ingot Cracking ,000 lb. Ingots 20% cracking rate $30,000 per recast 90 Potential Explanatory Variables Water composition Metal composition Process variables Other environmental variables Can we predict under what conditions ingots will crack? 14

15 Case Study II Car Insurance mature policies 65 Potential Predictors Can we find a pattern for the unprofitable policies? 15

16 Case Study III Breast Cancer Diagnosis Mammograms used as screening instrument Expensive radiologist read Inaccurate False positive and negative rates over 25% Over a decade, nearly 100% false positive rate Can we do better? Automatically read by a scanning algorithm Automatically diagnosed by a model 16

17 Why not Queries? Queries Describe Models promote understanding Models can be assessed both by their understanding and their predictions It s difficult to predict especially the future Queries are Event Driven Models are phenomenon driven Queries are reactive Models are proactive 17

18 What Happened on the Titanic? Class Crew First Second Third 18

19 Mosaic Plot 1 F D M F S M C C312 C 3 A 2 19

20 Models Powerful predictors for optimizing performance Powerful summaries for understanding Used to explore data set Are not perfect All models are wrong, but some are useful Statisticians, like artists, have the bad habit of falling in love with their models. 20

21 Tree Diagram M F Adult Child 3 1,2,C 2 or 3 1 or Crew 3 1 or 2 46% 93% 14% Crew 1st 27% 100% 23% 33% 21

22 Why Models? What s interesting? Most associated variables in the census What s associated with shampoo purchases? Beer and Diapers In the convenience stores we looked at, on Friday nights, purchases of beer and purchases of diapers are highly associated Conclusions? Actions? 22

23 Beer and Diapers Picture from Tandem TM ad 23

24 Toy Toy Problem train2[, i] 24 train2$y train2[, i] train2$y train2$y train2[, i] train2[, i] train2$y train2$y train2[, i] train2[, i] train2$y train2$y train2[, i] train2[, i] train2$y train2$y train2[, i] train2[, i] train2$y

25 Familiar Models Linear Regression 25

26 Logistic Regression 26

27 Linear Regression Term Estimate Std Error t Ratio Prob> t Intercept x <.0001 x <.0001 x x <.0001 x <.0001 x x x x x R-squared: 76.1% Train 73.3% Test 27

28 Stepwise Regression Term Estimate Std Error t Ratio Prob> t Intercept x <.0001 x <.0001 x x <.0001 x <.0001 x R-squared 76.0% on Train 73.4% Test 28

29 Stepwise 2 ND Order Model Term Estimate Std Error t Ratio Prob> t Intercept x <.0001 (x )*(x ) <.0001 x <.0001 (x )*(x ) <.0001 x <.0001 (x )*(x ) <.0001 x x <.0001 (x )*(x ) x x (x )*(x ) (x )*(x ) (x )*(x ) (x )*(x ) (x )*(x ) R-squared 90.0% Train 88.5% Test 29

30 Next Steps Higher order terms? When to stop? Transformations? Too simple: underfitting bias Too complex: inconsistent predictions, overfitting high variance Selecting models is Occam s razor Keep goals of interpretation vs. Prediction in mind 30

31 Tree Model x4< x1< x1< x4< x2< x5< x2< x3< x5< x2< x5< x3< x5< x5< x3< x2< x4< x4< x4< x4< x4< x2< x3< x4< x2< x4< x3< x3< x3< x3< x1< x3< x3< x3< x8< x4< x4< R squared 82.3% Train 67.2% Test

32 Feature Creation New predictor based on original predictors Often linear: z = α + b x i 1 1 b p x p Principal components Factor analysis Multidimensional scaling 32

33 Neural Nets Don t resemble the brain Are just a statistical model 33

34 A Single Neuron x1 x x3 x4 x Input (z1) s(z1) Output x0 z1 = x1 +.7x2 -.2x3 +.4x4 -.5x5 34

35 More exotic Neural networks x1 z1 x2 z2 y z3 Output layer Input layer Hidden layer 35

36 Running a Neural Net 36

37 Predictions for Example R squared 92.7% Train 90.6% Test 37

38 What Does This Get Us? Enormous flexibility Ability to fit anything Including noise Interpretation? 38

39 Case Study Warranty Data A new backpack inkjet printer is showing higher than expected warranty claims What are the important variables? What s going on? A neural networks shows that Zipcode is the most important predictor 39

40 Spatial Analysis Warranty Data showing problem with ink jet printer Use the model as a black box for variable selection 40

41 y MARS Multivariate Adaptive Regression Splines What do they do? Replace each step function in a tree model by a pair of linear functions y y x x x 41

42 MARS Variable Importance R-squared 95.0% Train 94.3% Test (96.3%) (95.8%) 42

43 MARS Function Output 43

44 Collaborative Filtering Goal: predict what movies people will like Data: list of movies each person has watched Lyle Andre, Starwars Ellen Andre, Starwars, Coeur en Hiver Fred Starwars, Batman Dean Starwars, Batman, Rambo Jason Coeur en Hiver, Chocolat 44

45 Data Base Data can be represented as a sparse matrix Andre Starwars Batman Rambo Coeur Chocolat Lyle y y Ellen y y y Fred y y Dean y y y Jason y y y Karen y????? Karen likes Andre. What else might she like? CDNow doubled responses 45

46 How Do We Really Start? Life is not so kind Categorical variables Missing data 500 variables, not variables where to start? 46

47 Where to Start? EDM Use a tree to find a smaller subset of variables to investigate Explore this set graphically Start the modeling process over Build model Compare model on small subset with full predictive model 47

48 Start With a Simple Model Maybe a Tree: x4< x2< x1< x5< x1< x5< x2< x1< x5< x2< x4< x4< x5< x5<

49 Automatic Models KXEN 49

50 PVA Results from KXEN 50

51 Combining Models -- Bagging Bagging (Bootstrap Aggregation) Bootstrap a data set repeatedly Take many versions of same model (e.g. tree) Form a committee of models Take majority rule of predictions 51

52 Combining Models -- Boosting Take the data and apply a simple classifier Reweight the data, weighting the misclassified data much higher. Reapply the classifier Repeat over and over The final prediction is a combination of the output of each classifier, weighted by the overall misclassification rate. Details in Freund, Y. Boosting a weak learning algorithm by majority, Information and Computation 121(2),

53 Breast Cancer Diagnosis 53

54 Results from Random Forest Results from 1000 splits of Training and Test data False Positive Rate False Negative Rate Tree 32.20% 33.70% Boosted Trees 24.90% 32.50% Random Forest 19.30% 28.80% Neural Netw ork 25.50% 31.70% Radiologists 22.40% 35.80% 54

55 Case Study Ingot failures Ingot cracking ,000 lb. Ingots 20% cracking rate $30,000 per recast 90 potential explanatory variables Water composition (reduced) Metal composition Process variables Other environmental variables 55

56 Model building process Model building Train Test Evaluate 56

57 Most Important Variable Take One Here we started with trees Alloy We know that OK, take two Yttrium What do you think is in the alloy? Third time s the charm? Selenium! OH! 57

58 Case Study Car Insurance Now that we have mature policies, can we find other factors to price policies better? 65 potential predictors Industry, vehicle age, color, numbers of vehicles, usage and location etc 58

59 Fast Fail Not every modeling effort is a success A model search can save lots of queries Data took 8 months to get ready Analyst spent 2 months exploring it A new model search program (KXEN) running for several hours found no out of sample predictive ability Tree model gave similar results 59

60 PVA Recap Remember predictor variables Need a way to trim this down Need an exploratory model Neural network? Tree? 60

61 Students in Data Mining Class Student #1 $15,024 Student #2 $14,695 Student #3 $14,345 61

62 Take Home Messages What a great time to be a Statistician! Problems are exciting Research is exciting Success in Data mining Requires Team Work Requires Flexibility in modeling Means that you Act on Your results Depends much more on the way you mine the data rather than the specific model or tool that you use Which method to use? Yes!! Have fun! 62

63 Thank you! 63

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