RESULTS OF THE NIPS 2006 MODEL SELECTION GAME Isabelle Guyon, Amir Saffari, Gideon Dror, Gavin Cawley, Olivier Guyon, and many other volunteers, see

Thanks

Part I INTRODUCTION

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.)

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.

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.

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.

Game rules Date started: October 1 st, Date ended: December 1 st, 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.

Datasets Dataset Domain Type Feat- ures Training Examples Validation Examples Test Examples ADA Marketing Dense GINA Digits Dense HIVA Drug discovery Dense NOVA Text classif. Sparse binary SYLVA Ecology Dense

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

Agnostic track on Dec. 1 st 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.

Part II PROTOCOL and SCORING

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.

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):  BER = abs(testBER – guessedBER)

CLOP CLOP=Challenge Learning Object Package. Based on the Spider developed at the Max Planck Institute. Two basic abstractions: –Data object –Model object

CLOP tutorial  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)}); At the Matlab prompt:

CLOP models

Preprocessing and FS

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

Part III RESULT ANALYSIS

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).

Method comparison (PPC)  BER Test BER Agnostic track no significant improvement so far

LS-SVM Gavin Cawley, July 2006

Logitboost Roman Lutz, July 2006

CLOP models (best entrant) DatasetCLOP models selected ADA 2*{sns,std,norm,gentleboost(neural),bias}; 2*{std,norm,gentleboost(kridge),bias}; 1*{rf,bias} 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} Juha Reunanen, cross-indexing-7 sns = shift’n’scale, std = standardize, norm = normalize (some details of hyperparameters not shown)

CLOP models (2 nd best entrant) DatasetCLOP models selected ADA {sns, std, norm, neural(units=5), bias} 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} Hugo Jair Escalante Balderas, BRun 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.

Danger of overfitting (PPC) BER Time (days) ADA GINA HIVA NOVA SYLVA Full line: test BER Dashed line: validation BER

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

Stats / CV / bounds ???

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

Part IV COMPETE NOW in the PRIOR KNOWLEDGE TRACK

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.

GINA 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. GINA is the digit database

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.

NOVA NOVA is the text classification database Task: Classify newsgroup s 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 words of vocabulary. Subject: Re: Goalie masks Lines: 21 Tom 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, along with 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) Pens logo, and a space for the "new" logo. A great mask done in by a goalie's superstition. Lori

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.)

How to enter? Enter results on any dataset in either track until March 1 st 2007 at 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.

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.

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?