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Pfizer HTS Machine Learning Algorithms: November 2002

Pfizer HTS Machine Learning Algorithms: November 2002. Paul Hsiung (hsiung+@cs.cmu.edu) Paul Komarek (komarek@cs.cmu.edu) Ting Liu (tingliu@cs.cmu.edu) Andrew W. Moore (awm@cs.cmu.edu) Auton Lab , Carnegie Mellon University School of Computer Science www.autonlab.org. Datasets.

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Pfizer HTS Machine Learning Algorithms: November 2002

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  1. Pfizer HTS Machine Learning Algorithms: November 2002 Paul Hsiung (hsiung+@cs.cmu.edu) Paul Komarek (komarek@cs.cmu.edu) Ting Liu (tingliu@cs.cmu.edu) Andrew W. Moore (awm@cs.cmu.edu) Auton Lab, Carnegie Mellon University School of Computer Science www.autonlab.org

  2. Datasets Auton Lab, www.autonlab.org

  3. Projections Auton Lab, www.autonlab.org

  4. Previous Algorithms Auton Lab, www.autonlab.org

  5. New Algorithms Auton Lab, www.autonlab.org

  6. Explicit False Positive Model Auton Lab, www.autonlab.org

  7. Explicit False Positive Model Auton Lab, www.autonlab.org

  8. Example in 2 dimensions: Decision Boundary Auton Lab, www.autonlab.org

  9. Example in 2 dimensions: 100 true positives Auton Lab, www.autonlab.org

  10. 100 true positives and 100 true negatives Auton Lab, www.autonlab.org

  11. 100 TP, 100 TN, 10 FP Auton Lab, www.autonlab.org

  12. Using regular logistic regression Auton Lab, www.autonlab.org

  13. Using EFP Model Auton Lab, www.autonlab.org

  14. Example: 10000 true positives Auton Lab, www.autonlab.org

  15. 10000 true positives, 10000 true negatives Auton Lab, www.autonlab.org

  16. 10000 TP, 10000 TN, 1000 FP Auton Lab, www.autonlab.org

  17. Using regular logistic regression Auton Lab, www.autonlab.org

  18. Using EFP Model Auton Lab, www.autonlab.org

  19. EFP Model Real Data Results K-fold Auton Lab, www.autonlab.org

  20. EFP Effect …Very impressive on Train1 / Test1 Auton Lab, www.autonlab.org

  21. Log X-axis Auton Lab, www.autonlab.org

  22. EFP Effect …Unimpressive on jun31 / jun32 Auton Lab, www.autonlab.org

  23. Super Model • Divide Training Set into Compartment A and Compartment B • Learn each of N models on Compartment A • Predict each of N models on Compartment B • Learn best weighting of opinions with Logistic Regression of Predictions on Compartment B • Apply the models and their weights to Test Data Auton Lab, www.autonlab.org

  24. Comparison Auton Lab, www.autonlab.org

  25. Log X-Axis Scale Auton Lab, www.autonlab.org

  26. Comparison on 100-dims Auton Lab, www.autonlab.org

  27. Log X-axis Auton Lab, www.autonlab.org

  28. Comparison on 10 dims Auton Lab, www.autonlab.org

  29. Log X-axis Auton Lab, www.autonlab.org

  30. NewKNN summary of results and timings Auton Lab, www.autonlab.org

  31. Auton Lab, www.autonlab.org

  32. Auton Lab, www.autonlab.org

  33. Auton Lab, www.autonlab.org

  34. Auton Lab, www.autonlab.org

  35. Auton Lab, www.autonlab.org

  36. Auton Lab, www.autonlab.org

  37. Auton Lab, www.autonlab.org

  38. Auton Lab, www.autonlab.org

  39. Auton Lab, www.autonlab.org

  40. Auton Lab, www.autonlab.org

  41. Auton Lab, www.autonlab.org

  42. Auton Lab, www.autonlab.org

  43. Auton Lab, www.autonlab.org

  44. Auton Lab, www.autonlab.org

  45. PLS summary of results • PLS projections did not do so well. • However, PLS as a predictor performed well,especially under train100/test100. • PLS is fast. The runtime varies from 1 to 10 minutes. • But PLS takes large amounts of memory. Impossibleto use in a sparse representation. (This is due to theupdate on each iteration.) Auton Lab, www.autonlab.org

  46. Auton Lab, www.autonlab.org

  47. Auton Lab, www.autonlab.org

  48. Auton Lab, www.autonlab.org

  49. Auton Lab, www.autonlab.org

  50. Auton Lab, www.autonlab.org

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