Jeong-Yoon Lee - Winning Data Science Competitions - Data Science Meetup - Oct 2015

by Data Science LA

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WINNING DATA SCIENCE COMPETITIONS Jeong-Yoon Lee @Conversion Logic

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DATA SCIENCE COMPETITIONS

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DATA SCIENCE COMPETITIONS Since 1997 2006 - 2009 Since 2010

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KAGGLE COMPETITIONS • 227 competitions since 2010 • 397,870 competitors • $3MM+ prize paid out

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KAGGLE COMPETITIONS

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Ph.D or CS degree is NOT required to win!

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Sang Su Lee @Retention Science Hang Li @Hulu Feng Qi @Quora (x-Hulu) KAGGLER IN TOWN

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COMPETITION STRUCTURE Training Data Test Data Feature Label Provided Submission Public LB Score Private LB Score

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BEST PRACTICES

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BEST PRACTICES • Feature Engineering • Machine Learning • Cross Validation • Ensemble

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FEATURE ENGINEERING • Numerical - Log, Log(1 + x), Normalization, Binarization • Categorical - One-hot-encode, TF-IDF (text), Weight-of-Evidence • Timeseries - Stats, FFT, MFCC (audio), ERP (EEG) • Numerical/Timeseries to Categorical - RF/GBM* * http://www.csie.ntu.edu.tw/~r01922136/kaggle-2014-criteo.pdf

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MACHINE LEARNING Algorithm Tool Note Gradient Boosting Machine XGBoost The best out-of-the-box solution Random Forests Scikit-Learn, randomForest Extra Trees Scikit-Learn Regularized Greedy Forest Tong Zhang’s Neural Networks Keras, Lasagne, MXNet Blends well with GBM. Best at image recognition competitions. Logistic/Linear Regression Scikit-Learn, Vowpal Wabbit Fastest. Good for ensemble. Support Vector Machine Scikit-Learn FTRL Vowpal Wabbit, tinrtgu’s Competitive solution for CTR estimation competitions Factorization Machine libFM Winning solution for KDD Cup 2012 Field-aware Factorization Machine libFFM Winning solution for CTR estimation competitions

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CROSS VALIDATION Training data are split into ﬁve folds where the sample size and dropout rate are preserved (stratiﬁed).

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ENSEMBLE - STACKING * for other types of ensemble, see http://mlwave.com/kaggle-ensembling-guide/

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KDD CUP 2015 WINNING SOLUTION* InterContinental Ensemble * originally presented by Kohei Ozaki, Mert Bay, and Tam T. Nguyen at KDD 2015

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KDD CUP 2015 • To predict dropouts in MOOCs. • Student activity logs and meta data are provided. • 821 teams. • $20K total prize.

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KDD CUP 2015 - DATA • 39 courses, 27K objects, 112K students. • 200K enrollments, 13MM activities.

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INTERCONTINENTAL ENSEMBLE Jeong-Yoon Lee Mert Bay Conversion Logic Song Chen AIG Andreas Toescher Michael Jahrer Opera Solutions Peng Yang Xiacong Zhou NetEase Tsinghua University Kohei Ozaki AIG Japan Tam T. Nguyen I2R A*STAR

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COLLABORATION

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STORY ABOUT LAST 28 HOURS

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Story about last 28 hours (1 of 3) 28 hours before the deadline: Ensemble framework and feature engineering worked great. But we were still in the 3rd place.

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Story about last 28 hours (2 of 3) 28 hours before the deadline: Continued working on feature engineering and single models with a new feature made a great improvement.

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Story about last 28 hours (3 of 3) 27 hours before the deadline: Ensemble models were trained with new single models and we jumped up to the 1st!

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FEATURE ENGINEERING

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Sequential Data Cube !me$ • hour$ • day$ • week$ • month$ event$ • navigate$ • access$ • problem$ • page$close$ • video$ • discussion$ • wiki$ object$ • user$ • course$ • source$ • user:course$ • …$

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Data Slicing and Dicing

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SINGLE MODEL TRAINING

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ALGORITHMS Algorithms # of Single Models Gradient Boosting Machine 26 Neural Network 14 Factorization Machine 12 Logistic Regression 6 Kernel Ridge Regression 2 Extra Trees 2 Random Forest 2 K-Nearest Neighbor 1 • A total of 64 single models were used in the ﬁnal solution.

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Single Model Training Training'Data' CV'Transformed'Data' Test'Data' Transformed' Test'Data' CV'Predic4on' Test'Predic4on' Transformed'Training' Data' Feature'Selec4on' Single'Model'Training'

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ENSEMBLE MODEL TRAINING

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Ensemble Model Training

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ENSEMBLE FRAMEWORK

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ENSEMBLE FRAMEWORK

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CV VS. LB SCORES LB AUC = 1.03 x CV AUC - 0.03

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IMPROVEMENTS BY ENSEMBLE 5-Fold CV Public Leaderboard Single Best 0.906721 0.907765 Stage-I Best 0.907688 0.908796 Stage-II Best 0.907968 N/A Stage-III Best 0.908194 0.909181 • Single best to Stage-III ensemble best score is 0.0014 improvement!

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SUMMARY • The following items helped tremendously in winning KDD Cup 2015: • Good team dynamics and collaboration. • Hand crafted features. • Multi-stage ensemble.

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Thank you!

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[email protected] @jeongyoonlee linkedin.com/in/jeongyoonlee kaggle.com/jeongyoonlee github.com/jeongyoonlee/