Gradient Boosting Machines (GBM): From Zero to Hero (with R and Python Code) - Budapest BI Forum, Budapest, Nov 2019

Ce8e94cc306ba164175f693fb01aa8b0?s=47 szilard
November 01, 2019

Gradient Boosting Machines (GBM): From Zero to Hero (with R and Python Code) - Budapest BI Forum, Budapest, Nov 2019

Ce8e94cc306ba164175f693fb01aa8b0?s=128

szilard

November 01, 2019
Tweet

Transcript

  1. 1.

    Gradient Boosting Machines (GBM): From Zero to Hero (with R

    and Python Code) Szilard Pafka, PhD Chief Scientist, Epoch (USA) Budapest BI Forum Nov 2019
  2. 2.
  3. 3.
  4. 4.

    Disclaimer: I am not representing my employer (Epoch) in this

    talk I cannot confirm nor deny if Epoch is using any of the methods, tools, results etc. mentioned in this talk
  5. 8.
  6. 9.
  7. 10.
  8. 11.
  9. 12.
  10. 14.
  11. 15.
  12. 16.

    ...

  13. 17.
  14. 18.
  15. 19.
  16. 20.
  17. 21.
  18. 22.
  19. 26.

    Supervised Learning Data: X (n obs, p features), y (labels)

    Regression, classification Train/learn/fit f from data (model) Score: for new x, get f(x) Algos: LR, k-NN, DT, RF, GBM, NN/DL, SVM, NB… Goal: max acc/min err new data Metrics: MSE, AUC (ROC) Bad: measure on train set. Need: test set/cross-validation (CV) Hyperparameters, model capacity, overfitting Regularization Model selection Hyperparameter search (grid, random) Ensembles
  20. 27.

    Supervised Learning Data: X (n obs, p features), y (labels)

    Regression, classification Train/learn/fit f from data (model) Score: for new x, get f(x) Algos: LR, k-NN, DT, RF, GBM, NN/DL, SVM, NB… Goal: max acc/min err new data Metrics: MSE, AUC (ROC) Bad: measure on train set. Need: test set/cross-validation (CV) Hyperparameters, model capacity, overfitting Regularization Model selection Hyperparameter search (grid, random) Ensembles
  21. 28.
  22. 29.
  23. 32.
  24. 33.
  25. 34.
  26. 35.
  27. 36.
  28. 37.
  29. 38.
  30. 39.
  31. 40.
  32. 41.
  33. 42.
  34. 43.
  35. 44.
  36. 45.
  37. 46.
  38. 47.
  39. 48.
  40. 49.
  41. 50.
  42. 52.
  43. 53.
  44. 54.

    Live Demo Summary of the demo for those reading just

    the slides (e.g. those who did not attend the talk):
  45. 55.
  46. 56.
  47. 57.
  48. 58.
  49. 59.
  50. 60.
  51. 61.
  52. 62.
  53. 63.
  54. 64.
  55. 65.
  56. 66.
  57. 67.
  58. 70.
  59. 71.
  60. 72.
  61. 73.
  62. 74.
  63. 75.