Mahout classifier tour
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The document discusses Mahout, an open source machine learning library. It describes Mahout's capabilities for recommendations, clustering, classification and other machine learning tasks. Specifically, it covers Mahout's online training algorithms like logistic regression which can train models on small to moderate sized datasets efficiently with low overhead. The document provides examples and details Mahout's approach to online learning and model training.
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Mahout classifier tour
- 1. Mahout 1 Wednesday, March 16, 2011
- 2. Mahout Scalable Data Mining for Everybody 1 Wednesday, March 16, 2011
- 3. What is Mahout • Recommendations (people who x this also x that) • Clustering (segment data into groups of) • Classification (learn decision making from examples) • Stuff (LDA, SVD, frequent item-set, math) 2 Wednesday, March 16, 2011
- 4. What is Mahout? • Recommendations (people who x this also x that) • Clustering (segment data into groups of) •Classification (learn decision making from examples) • Stuff (LDA, SVM, frequent item-set, math) 3 Wednesday, March 16, 2011
- 5. Classification in Detail • Naive Bayes Family • Hadoop based training • Decision Forests • Hadoop based training • Logistic Regression (aka SGD) • fast on-line (sequential) training 4 Wednesday, March 16, 2011
- 6. Classification in Detail • Naive Bayes Family • Hadoop based training • Decision Forests • Hadoop based training •Logistic Regression (aka SGD) •fast on-line (sequential) training 5 Wednesday, March 16, 2011
- 7. So What? Online training has low overhead for small and moderate size data-sets 6 Wednesday, March 16, 2011
- 8. So What? Online training has low overhead for small and moderate size data-sets 6 Wednesday, March 16, 2011
- 9. So What? Online training has low overhead for small and moderate size data-sets 6 Wednesday, March 16, 2011
- 10. So What? Online training has low overhead for small and moderate size data-sets 6 Wednesday, March 16, 2011
- 11. So What? Online training has low overhead for small and moderate size data-sets big starts here 6 Wednesday, March 16, 2011
- 12. An Example 7 Wednesday, March 16, 2011
- 13. An Example 7 Wednesday, March 16, 2011
- 14. An Example 7 Wednesday, March 16, 2011
- 15. An Example 7 Wednesday, March 16, 2011
- 16. An Example 7 Wednesday, March 16, 2011
- 17. An Example 7 Wednesday, March 16, 2011
- 18. An Example 7 Wednesday, March 16, 2011
- 19. And Another From: Dr. Paul Acquah Dear Sir, Re: Proposal for over-invoice Contract Benevolence Based on information gathered from the India hospital directory, I am pleased to propose a confidential business deal for our mutual benefit. I have in my possession, instruments (documentation) to transfer the sum of 33,100,000.00 eur thirty-three million one hundred thousand euros, only) into a foreign company's bank account for our favor. ... 8 Wednesday, March 16, 2011
- 20. And Another Date: Thu, May 20, 2010 at 10:51 AM From: George <george@fumble-tech.com> Hi Ted, was a pleasure talking to you last night at the Hadoop User Group. I liked the idea of going for lunch together. Are you available tomorrow (Friday) at noon? 8 Wednesday, March 16, 2011
- 21. And Another Date: Thu, May 20, 2010 at 10:51 AM From: George <george@fumble-tech.com> Hi Ted, was a pleasure talking to you last night at the Hadoop User Group. I liked the idea of going for lunch together. Are you available tomorrow (Friday) at noon? 8 Wednesday, March 16, 2011
- 22. And Another Date: Thu, May 20, 2010 at 10:51 AM From: George <george@fumble-tech.com> Hi Ted, was a pleasure talking to you last night at the Hadoop User Group. I liked the idea of going for lunch together. Are you available tomorrow (Friday) at noon? 8 Wednesday, March 16, 2011
- 23. Mahout’s SGD • Learns on-line per example • O(1) memory • O(1) time per training example • Sequential implementation • fast, but not parallel 9 Wednesday, March 16, 2011
- 24. Special Features • Hashed feature encoding • Per-term annealing • learn the boring stuff once • Auto-magical learning knob turning • learns correct learning rate, learns correct learning rate for learning learning rate, ... 10 Wednesday, March 16, 2011
- 25. Feature Encoding 11 Wednesday, March 16, 2011
- 26. Feature Encoding 11 Wednesday, March 16, 2011
- 27. Hashed Encoding 12 Wednesday, March 16, 2011
- 28. Feature Collisions 13 Wednesday, March 16, 2011
- 29. Learning Rate Annealing Learning Rate # training examples seen 14 Wednesday, March 16, 2011
- 30. Per-term Annealing Learning Rate # training examples seen 15 Wednesday, March 16, 2011
- 31. Per-term Annealing Learning Rate # training examples seen Common Feature 15 Wednesday, March 16, 2011
- 32. Per-term Annealing Learning Rate # training examples seen Rare Feature 15 Wednesday, March 16, 2011
- 33. General Structure • OnlineLogisticRegression • Traditional logistic regression • Stochastic Gradient Descent • Per term annealing • Too fast (for the disk + encoder) 16 Wednesday, March 16, 2011
- 34. Next Level • CrossFoldLearner • contains multiple primitive learners • online cross validation • 5x more work 17 Wednesday, March 16, 2011
- 35. And again • AdaptiveLogisticRegression • 20 x CrossFoldLearner • evolves good learning and regularization rates • 100 x more work than basic learner • still faster than disk + encoding 18 Wednesday, March 16, 2011
- 36. A comparison • Traditional view • 400 x (read + OLR) • Revised Mahout view • 1 x (read + mu x 100 x OLR) x eta • mu = efficiency from killing losers early • eta = efficiency from stopping early 19 Wednesday, March 16, 2011
- 37. Deployment • Training • ModelSerializer.writeBinary(..., model) • Deployment • m = ModelSerializer.readBinary(...) • r = m.classifyScalar(featureVector) 20 Wednesday, March 16, 2011
- 38. The Upshot • One machine can go fast • SITM trains in 2 billion examples in 3 hours • Deployability pays off big • simple sample server farm 21 Wednesday, March 16, 2011