RESULTS OF THE NIPS 2006 MODEL SELECTION GAME Isabelle Guyon, Amir Saffari, Gideon Dror, Gavin Cawley, Olivier Guyon,

1. RESULTS OF THE NIPS 2006 MODEL SELECTION GAME Isabelle Guyon, Amir Saffari, Gideon Dror, Gavin Cawley, Olivier Guyon, and many other volunteers, see http://www.agnostic.inf.ethz.ch/credits.php

2. Thanks

3. Part I INTRODUCTION

4. Model selection • Selecting models (neural net, decision tree, SVM, …) • Selecting hyperparameters (number of hidden units, weight decay/ridge, kernel parameters, …) • Selecting variables or features (space dimensionality reduction.) • Selecting patterns (data cleaning, data reduction, e.g by clustering.)

5. Performance prediction challenge How good are you at predicting how good you are? • Practically important in pilot studies. • Good performance predictions render model selection trivial.

6. Model Selection Game Find which model works best in a well controlled environment. • A given “sandbox”: the CLOP Matlab® toolbox. • Focus only on devising model selection strategy. • Same datasets as the performance prediction challenge, but “reshuffled” • Two $500 prizes offered.

7. Agnostic Learning vs. Prior Knowledge challenge When everything else fails, ask for additional domain knowledge… • Two tracks: • Agnostic learning: Preprocessed datasets in a nice “feature-based” representation, but no knowledge about the identity of the features. • Prior knowledge: Raw data, sometimes not in a feature-based representation. Information given about the nature and structure of the data.

8. Game rules • Date started: October 1st, 2006. • Date ended: December 1st, 2006 • Duration: 3 months. • Submit in Agnostic track only. • Optionally use CLOP or Spider. • Five last complete entries ranked: • Total ALvsPK challenge entrants: 22. • Total ALvsPK developement entries: 546. • Number of game ranked participants: 10. • Number of game ranked submissions: 39.

9. Datasets Type Dataset Validation Examples Domain Feat-ures Training Examples Test Examples Dense ADA 415 Marketing 48 4147 41471 Dense GINA 315 Digits 970 3153 31532 Dense HIVA 384 Drug discovery 1617 3845 38449 Sparse binary NOVA 175 Text classif. 16969 1754 17537 Dense SYLVA 1308 Ecology 216 13086 130858 http://www.agnostic.inf.ethz.ch

10. Baseline BER distribution(Performance prediction challenge, 145 entrants) Test BER

11. Agnostic track on Dec. 1st 2006 • Yellow: used a CLOP model • CLOP prize winner: Juha Reunanen (both ave. rank and ave. BER) • Best ave. BER still held by Reference (Gavin Cawley) with the_bad.

12. Part II PROTOCOL and SCORING

13. Protocol • Data split: training/validation/test. • Data proportions: 10/1/100. • Online feed-back on validation data. • Validation label release: not yet; one month before end of challenge. • Final ranking on test data using the five last complete submissions for each entrant.

14. Performance metrics • Balanced Error Rate (BER): average of error rates of positive class and negative class. • Area Under the ROC Curve (AUC). • Guess error (for the performance prediction challenge only): dBER = abs(testBER – guessedBER)

15. CLOP • CLOP=Challenge Learning Object Package. • Based on the Spider developed at the Max Planck Institute. • Two basic abstractions: • Data object • Model object http://www.agnostic.inf.ethz.ch/models.php

16. CLOP tutorial At the Matlab prompt: • D=data(X,Y); • hyper = {'degree=3', 'shrinkage=0.1'}; • model = kridge(hyper); • [resu, model] = train(model, D); • tresu = test(model, testD); • model = chain({standardize,kridge(hyper)});

17. CLOP models

18. Preprocessing and FS

19. Model grouping for k=1:10 base_model{k}=chain({standardize, naive}); end my_model=ensemble(base_model);

20. Part III RESULT ANALYSIS

21. What did we expect? • Learn about new competitive machine learning techniques. • Identify competitive methods of performance prediction, model selection, and ensemble learning (theory put into practice). • Drive research in the direction of refining such methods (on-going benchmark).

22. Method comparison (PPC) Agnostic track no significant improvement so far dBER Test BER

23. LS-SVM Gavin Cawley, July 2006

24. Logitboost Roman Lutz, July 2006

25. Dataset CLOP models selected 2*{sns,std,norm,gentleboost(neural),bias}; 2*{std,norm,gentleboost(kridge),bias}; 1*{rf,bias} ADA GINA 6*{std,gs,svc(degree=1)}; 3*{std,svc(degree=2)} HIVA 3*{norm,svc(degree=1),bias} NOVA 5*{norm,gentleboost(kridge),bias} SYLVA 4*{std,norm,gentleboost(neural),bias}; 4*{std,neural}; 1*{rf,bias} CLOP models (best entrant) Juha Reunanen, cross-indexing-7 sns = shift’n’scale, std = standardize, norm = normalize(some details of hyperparameters not shown)

26. Dataset CLOP models selected {sns, std, norm, neural(units=5), bias} ADA GINA {norm, svc(degree=5, shrinkage=0.01), bias} HIVA {std, norm, gentleboost(kridge), bias} NOVA {norm,gentleboost(neural), bias} SYLVA {std, norm, neural(units=1), bias} CLOP models (2nd best entrant) Hugo Jair Escalante Balderas, BRun2311062 sns = shift’n’scale, std = standardize, norm = normalize(some details of hyperparameters not shown) Note: entry Boosting_1_001_x900 gave better results, but was older.

27. Danger of overfitting (PPC) Full line: test BER Dashed line: validation BER 0.5 0.45 0.4 0.35 HIVA 0.3 BER 0.25 0.2 ADA 0.15 0.1 NOVA GINA 0.05 SYLVA 0 0 20 40 60 80 100 120 140 160 Time (days)

28. Two best CLOP entrants (game) Ave. test BER H._Jair_Escalante Juha Reunanen Time Statistically significant difference for 3/5 datasets.

29. Stats / CV / bounds ???

30. Top ranking methods • Performance prediction: • CV with many splits 90% train / 10% validation • Nested CV loops • Model selection • Performance prediction challenge • Use of a single model family • Regularized risk / Bayesian priors • Ensemble methods • Nested CV loops, computationally efficient with with VLOO • Model selection game • Cross-indexing • Particle swarm

31. Part IV COMPETE NOW in the PRIOR KNOWLEDGE TRACK

32. ADA ADA is the marketing database • Task: Discover high revenue people from census data. Two-class pb. • Source: Census bureau, “Adult” database from the UCI machine-learning repository. • Features: 14 original attributes including age, workclass,  education, education, marital status, occupation, native country. Continuous, binary and categorical features.

33. GINA GINA is the digit database • Task: Handwritten digit recognition. Separate the odd from the even digits. Two-class pb. with heterogeneous classes. • Source: MNIST database formatted by LeCun and Cortes. • Features: 28x28 pixel map.

34. HIVA HIVA is the HIV database • Task: Find compounds active against the AIDS HIV infection. We brought it back to a two-class pb. (active vs. inactive), but provide the original labels (active, moderately active, and inactive). • Data source: National Cancer Inst. • Data representation: The compounds are represented by their 3d molecular structure.

35. NOVA Subject: Re: Goalie masksLines: 21Tom Barrasso wore a great mask, one time, last season.  He unveiled it at a game in Boston.  It was all black, with Pgh city scenes on it. The "Golden Triangle" graced the top, alongwith a steel mill on one side and the Civic Arena on the other.   On the back of the helmet was the old Pens' logo the current (at the time) Penslogo, and a space for the "new" logo.A great mask done in by a goalie's superstition.Lori NOVA is the text classification database • Task: Classify newsgroup emails into politics or religion vs. other topics. • Source: The 20-Newsgroup dataset from in the UCI machine-learning repository. • Data representation : The raw text with an estimated 17000 words of vocabulary.

36. SYLVA SYLVA is the ecology database • Task: Classify forest cover types into Ponderosa pine vs. everything else. • Source: US Forest Service (USFS). • Data representation: Forest cover type for 30 x 30 meter cells encoded with 108 features (elavation, hill shade, wilderness type, soil type, etc.)

37. How to enter? • Enter results on any dataset in either track until March 1st 2007 at http://www.agnostic.inf.ethz.ch. • Only “complete” entries (on 5 datasets) will be ranked. The 5 last will count. • Seven prizes: • Best overall agnostic entry. • Best overall prior knowledge entry. • Best prior knowledge result in each dataset (5 prizes). • Best paper.

38. Conclusions • Less participation volume as in the previous challenges: • Entry level higher • Other on-going competitions • Top methods in agnostic track as before • LS-SVMs and boosted logistic trees • Top ranking entries closely followed by CLOP entries showing great advances in model selection. • Todo: upgrade CLOP with LS-SVMs and logitboost.

39. Open problems Bridge the gap between theory and practice… • What are the best estimators of the variance of CV? • What should k be in k-fold? • Are other cross-validation methods better than k-fold (e.g bootstrap, 5x2CV)? • Are there better “hybrid” methods? • What search strategies are best? • More than 2 levels of inference?