Square's Machine Learning Infrastructure and Applications - Rong Yan
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Square's Machine Learning Infrastructure and Applications - Rong Yan
- 1. May 15, 2014 ! Rong Yan Machine Learning @ Square
- 5. Our Mission Make commerce easy.
- 6. Payment Data Commerce The Next Big Thing
- 8. Offline and Online Amount Location Item Desc. Card # Credit Score Friends Activity History Inventory
- 9. Sales Volume
- 10. Haircut Price
- 11. Turn Data into Business Value Fraud Detection Business Insight Customer Relation Information Discovery
- 12. Fraud Detection @ Square
- 13. Fraud Detection in the payment flow Bank Clears for settlement Suspect ~2000 sellers Risk Ops Transaction review 150,000 active sellers per day Risk ML Fraud Detection
- 15. Card not present: Yes Pan Diversity: 0.05 Use iPhone: No Feature Generation
- 16. Easy to interpret ! Dimension reduction ! ! Very powerful in ensemble Decline Rate >= 0.1 NoYes Amount <= $10000 NoYes Business Type = Auto repair NoYes 0.9 0.6 Decision Tree Model
- 17. Random Forests: Decision Tree Ensemble Decline Rate <= 0.1 NoYes Amount <= $10000 Business Type = Auto repair 0.9 0.6 Tree 1 Tree N Breiman, Leo (2001). "Random Forests". Machine Learning 45 (1): 5–32. Mode for classification = Bad Average for regression = 0.63 NoYes NoYes Success Rate <= 0.2 NoYes Age >= 20 Amount <= $1000 0.4 0.7 NoYes NoYes Decline Rate <= 0.3 NoYes Amount <= $20000 Age <= 22 0.8 0.6 NoYes NoYes Tree 2 Bad, 0.9 Good, 0.4 Bad, 0.6
- 18. Random Forests - Build each Tree All data
- 19. Breiman, Leo (2001). "Random Forests". Machine Learning 45 (1): 5–32. All data Samples Random Forests - Build each Tree
- 20. Breiman, Leo (2001). "Random Forests". Machine Learning 45 (1): 5–32. Features Dollar Amount Connected with bad user Business Type Decline Rate Time of Day Location Randomly select sqrt(n) features All data Samples Random Forests - Build each Tree
- 21. Breiman, Leo (2001). "Random Forests". Machine Learning 45 (1): 5–32. Features Dollar Amount Connected with bad user Business Type Decline Rate Time of Day Location Randomly select sqrt(n) features Best split: feature and value Decline Rate <= 0.1 NoYes 0.4 0.6 All data Samples Random Forests - Build each Tree
- 22. Breiman, Leo (2001). "Random Forests". Machine Learning 45 (1): 5–32. Features Dollar Amount Connected with bad user Business Type Decline Rate Time of Day Location Randomly select sqrt(n) features Best split: feature and value Decline Rate <= 0.1 NoYes 0.4 0.6 All data Samples Grow Tree Grow Tree Random Forests - Build each Tree
- 23. Breiman, Leo (2001). "Random Forests". Machine Learning 45 (1): 5–32. Features Dollar Amount Connected with bad user Business Type Decline Rate Time of Day Location Randomly select sqrt(n) features Best split: feature and value Decline Rate <= 0.1 NoYes 0.4 0.6 All data Samples Grow Tree Grow Tree When sample size is small STOP Random Forests - Build each Tree
- 24. Breiman, Leo (2001). "Random Forests". Machine Learning 45 (1): 5–32. Features Dollar Amount Connected with bad user Business Type Decline Rate Time of Day Location Randomly select sqrt(n) features Best split: feature and value Decline Rate <= 0.1 NoYes 0.4 0.6 All data Samples Grow Tree Grow Tree When sample size is small STOP Repeat these steps multiple times to create a forest Random Forests - Build each Tree
- 26. Boosting Trees Tree 1 Tree 2 Help Tree 1
- 27. Boosting Trees Tree 1 Tree 2 Tree 3 Tree 4 Help Tree 1 Help Tree 1, 2 Help Tree 1, 2, 3 Stop when no help needed 0 weights all samples
- 28. Boosting Trees Tree 1 Tree 2 Tree 3 Tree 4 Help Tree 1 Help Tree 1, 2 Help Tree 1, 2, 3 8.0 -2.0 1.0 0.57.5 = + + +
- 29. Boosting Trees - Algorithm Objective function: Loss Friedman, J. H. "Greedy Function Approximation: A Gradient Boosting Machine." 1999
- 30. Precision at a fixed recall level Results - Precision Model April May June Random Forest 76% 77% 80% Boosting Trees 85% 82% 88% +11.8% +6.5% +10%
- 31. Results - Fraud Detection Recall # Payments to Reject Fraud$Prevented Easy Hard Medium
- 32. Data Sampling Highly biased in label distribution - Less than 1 in 1000 ! Weighted training - Higher weights on positive samples => oscillation - Lower weights on negative samples => no real gain ! Solution - Keep negative:positive ratio to be 3:1 - 10:1 - Scale the final model if calibration is needed ! Fewer data requires fewer resources to train ! Observed +10% improvement from 20:1 to 3:1
- 34. ‣ Ruby-on-Rails + MySQL ‣ MySQL replication ‣ Tied to production schema ‣ Hard to do complex analysis Startup Architecture
- 35. ‣ Jave services ‣ APIs ‣ HDFS Scale it up: SOA + Data Warehouse
- 36. Scale it up: Data Transport ‣ Append-only feeds ‣ Kafka ‣ Replication ‣ Protocol buffers
- 38. Parallel Environments and Data Integrity Blue Green VIPupstream
- 39. Square Random Forest Learning Management Recommendation Other ML @ Square
- 40. Square Random Forest RF Learner Implementation Time (Train / Test) RiskML Random Forest (Built on Scikit-Learn) C / Cython / Python (Open Source + Square Code) 72 minutes WiseRF C++ (Proprietary) 23 minutes Square Random Forest Java (Square Code) 15 minutes Note: time reported on 3M training and 15M testing data
- 41. Learning Management System ‣ Support non-sophisticated users ‣ Fast ad-hoc analytics ‣ Accessible to everyone for easy model generation and evaluation ‣ Tracks results to ensure different models can be compared
- 42. Square Market Recommendation 10x conversion rate vs. random baseline