Machine Learning Model Comparison and Evaluation Techniques
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This document discusses machine learning model comparison and evaluation. It describes how the rendezvous architecture in MapR makes evaluation easier by collecting metrics on model performance and allowing direct comparison of models. It also discusses challenges like reject inferencing and the need to balance exploration of new models with exploitation of existing models. The document provides recommendations for change detection and analyzing latency distributions to better evaluate models over time.
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Machine Learning Model Comparison and Evaluation Techniques
- 1. © 2017 MapR Technologies 1 Machine Learning Comparison and Evaluation
- 2. © 2017 MapR Technologies 2 Contact Information Ted Dunning, PhD Chief Application Architect, MapR Technologies Board Member, Apache Software Foundation O’Reilly author Email tdunning@mapr.com ted@apache.org Twitter @ted_dunning
- 3. © 2017 MapR Technologies 3 Machine Learning Everywhere Image courtesy Mtell used with permission.Images © Ellen Friedman.
- 4. © 2017 MapR Technologies 4 Scores ArchiveDecoy m1 m2 m3 Features / profiles InputRaw
- 5. © 2017 MapR Technologies 5 ResultsRendezvousScores ArchiveDecoy m1 m2 m3 Features / profiles InputRaw
- 6. © 2017 MapR Technologies 6 Metrics Metrics ResultsRendezvousScores ArchiveDecoy m1 m2 m3 Features / profiles InputRaw
- 7. © 2017 MapR Technologies 7 Let’s talk about how the rendezvous architecture makes evaluation easier
- 8. © 2017 MapR Technologies 8 Decoy Model in the Rendezvous Architecture Input Scores Decoy Model 2 Model 3 Archive • Looks like a server, but it just archives inputs • Safe in a good streaming environment, less safe without good isolation
- 9. © 2017 MapR Technologies 9 Other Data Collected in Rendezvous • Request ID + Input data • All output scores • Evaluation latency • Round trip latency • Rendezvous choices
- 10. © 2017 MapR Technologies 10 Direct Model Comparison • Don’t need ground truth to compare models at a gross level • For uncalibrated models, score quantiles are useful • For mature models, most results will be very similar – Large differences from known good models cannot be good • Ultimately, ground truth is important – But only for cases where scores differ significantly
- 11. © 2017 MapR Technologies 11 Direct Model Differencing −2 0 2 4 0246 Raw Scores 0.0 0.5 1.0 0.00.51.0 Q−Q plot
- 12. © 2017 MapR Technologies 12 Direct Model Differencing −2 0 2 4 0246 Raw Scores 0.0 0.5 1.0 0.00.51.0 Q−Q plot Scales may differ radically
- 13. © 2017 MapR Technologies 13 Direct Model Differencing −2 0 2 4 0246 Raw Scores 0.0 0.5 1.0 0.00.51.0 Q−Q plot Scales may differ radically Quantiles correct scaling
- 14. © 2017 MapR Technologies 14 Direct Model Differencing −2 0 2 4 0246 Raw Scores 0.0 0.5 1.0 0.00.51.0 Q−Q plot Scales may differ radically Quantiles correct scaling Perfect match on high scores
- 15. © 2017 MapR Technologies 15 Reject Inferencing • Today’s model selects tomorrows training data • Safe decisions often prevent data collection – Fraud flag prevents the transaction – Recommendation ranking has the same effect • The model winds up confirming what it already knows • Model comparison has same problem – Champion says reject, challenger says retain
- 16. © 2017 MapR Technologies 16 Reject Inferencing Solution • We must balance EXPLORATION – Calling a bluff to look at ground truth • Versus EXPLOITATION – Doing what we think is right • Exploration costs us because we make worse decisions – But it can help make better decisions later • Exploitation costs us because we don’t learn better answers – But it is the best we know now
- 17. © 2017 MapR Technologies 17 Multi-Armed Bandits • Classic formulation for explore/exploit trade-offs • Thompson sampling is very good option • Simple dithering may be good enough • Key intuition is that we don’t need to perfectly characterize losers … once we know they are losers, we don’t care • Variant for ranking also good for model evaluation – Also used to rank reddit comments
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- 26. © 2017 MapR Technologies 26 Some Warnings • Bad models can be good explorers • That can make other models look better • Offline evaluation is fine, but you don’t know what would have happened … real innovation has high error bars • Where models all agree, we learning nothing • In the end, it is differences that matter the most
- 27. © 2017 MapR Technologies 27 Having complete and precise history is golden for offline comparisons
- 28. © 2017 MapR Technologies 28 Allowing the rendezvous server to do Thompson sampling is even better
- 29. © 2017 MapR Technologies 29 Change Detection • Model comparison is all fine and good until the world changes • And the world will change • One of the most sensitive indicators is score distribution for a good model – T-digest is very effective for sketching distributions, especially in tails – Compare current vs historical distribution using q-q or KS
- 30. © 2017 MapR Technologies 30 Analyzing latencies
- 31. © 2017 MapR Technologies 31 Hotel Room Latencies • These are ping latencies from my hotel • Looks pretty good, right? • But what about longer term? 208.302 198.571 185.099 191.258 201.392 214.738 197.389 187.749 201.693 186.762 185.296 186.390 183.960 188.060 190.763 > mean(y$t[i]) [1] 198.6047 > sd(y$t[i]) [1] 71.43965
- 32. © 2017 MapR Technologies 32 Not So Fast …
- 33. © 2017 MapR Technologies 33 This is long-tailed land
- 34. © 2017 MapR Technologies 34 This is long-tailed land You have to know the distribution of values
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- 36. © 2017 MapR Technologies 36 A single number is simply not enough
- 37. © 2017 MapR Technologies 37 And this histogram is hard to read
- 38. © 2017 MapR Technologies 38 Idea – Exponential Bins • Suppose we want relative accuracy in measurement space • Latencies are positive and only matter within a few percent – 1.1 ms versus 1.0 ms – 1100 ms versus 1000 ms • We can cheat by using floating point representations – Compute bin using magic – Adjust bins slightly using more magic – Count
- 39. © 2017 MapR Technologies 39 FloatHistogram • Assume all measurements are in the range • Divide this range into power of 2 sub-ranges • Sub-divide each sub-range evenly with steps – is typical • Relative error is bounded in measurement space
- 40. © 2017 MapR Technologies 40 FloatHistogram • Assume all measurements are in the range • Divide this range into power of 2 sub-ranges • Sub-divide each sub-range evenly with steps – is typical • Relative error is bounded in measurement space • Bin index can be computed using FP representation!
- 41. © 2017 MapR Technologies 41 What about visualization?
- 42. © 2017 MapR Technologies 42 Can’t see small count bars
- 43. © 2017 MapR Technologies 43 Good Results
- 44. © 2017 MapR Technologies 44 Bad Results – 1% of measurements are 3x bigger
- 45. © 2017 MapR Technologies 45 Bad Results – 1% of measurements are 3x bigger
- 46. © 2017 MapR Technologies 46 Uniform Bins
- 47. © 2017 MapR Technologies 47 FloatHistogram Bins
- 48. © 2017 MapR Technologies 48 With FloatHistogram
- 49. © 2017 MapR Technologies 49 Sign Up for Next Workshop in the MLL Series by Ted Dunning, Chief Applications Architect at MapR: Machine Learning in the Enterprise: How to do model management in production http://bit.ly/mapr-machine-learning-logistics-series
- 50. © 2017 MapR Technologies 50 Additional Resources O’Reilly report by Ted Dunning & Ellen Friedman © March 2017 Read free courtesy of MapR: https://mapr.com/geo-distribution-big-data-and-analytics/ O’Reilly book by Ted Dunning & Ellen Friedman © March 2016 Read free courtesy of MapR: https://mapr.com/streaming-architecture-using- apache-kafka-mapr-streams/
- 51. © 2017 MapR Technologies 51 Additional Resources O’Reilly book by Ted Dunning & Ellen Friedman © June 2014 Read free courtesy of MapR: https://mapr.com/practical-machine-learning- new-look-anomaly-detection/ O’Reilly book by Ellen Friedman & Ted Dunning © February 2014 Read free courtesy of MapR: https://mapr.com/practical-machine-learning/
- 52. © 2017 MapR Technologies 52 Additional Resources by Ellen Friedman 8 Aug 2017 on MapR blog: https://mapr.com/blog/tensorflow-mxnet-caffe-h2o-which-ml-best/ Interview by Thor Olavsrud in CIO: https://www.cio.com.au/article/630299/ what-dataops-collaborative-cross- functional-analytics/?fp=16&fpid=1
- 53. © 2017 MapR Technologies 53 Read more in new book on model management: New O’Reilly book by Ted Dunning & Ellen Friedman© September 2017 Download free pdf courtesy of MapR: https://mapr.com/ebook/machine-learning-logistics/
- 54. © 2017 MapR Technologies 54 Please support women in tech – help build girls’ dreams of what they can accomplish © Ellen Friedman 2015#womenintech #datawomen
- 55. © 2017 MapR Technologies 55 Q&A @mapr Maprtechnologies tdunning@mapr.com ENGAGE WITH US @ted_dunning