1 / 52

SAS 9.1 Decision Tree

SAS 9.1 Decision Tree. 데이터마이닝 방법론 강현철 외 자유아카데미 . 2006/03/01. Classification Tree. Classification Tree (Chapter3_1). Classification Tree: Input Data Source (HMEQ). Classification Tree: Input Data Source (BAD  Target). Classification Tree: Input Data Source (View Distribution).

gage
Download Presentation

SAS 9.1 Decision Tree

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. SAS 9.1Decision Tree 데이터마이닝 방법론 강현철 외 자유아카데미. 2006/03/01

  2. Classification Tree

  3. Classification Tree (Chapter3_1)

  4. Classification Tree: Input Data Source (HMEQ)

  5. Classification Tree: Input Data Source (BAD  Target)

  6. Classification Tree: Input Data Source (View Distribution)

  7. Classification Tree: Input Data Source (Interval Variables)

  8. Classification Tree: Data Partition (70:30:0)

  9. Classification Tree: Assessment (Run)

  10. Classification Tree: Lift Chart (Tree > Reg, Neural)

  11. Classification Tree: Lift Chart (Tree > Reg, Neural)

  12. Classification Tree: Tree (Results) (0.1130 error rates for validation data)

  13. Classification Tree: Tree (View/Tree)

  14. Classification Tree: Tree (Results) (도구/색상정의)

  15. Classification Tree: Tree (Results)

  16. Classification Tree: Tree (View/Tree)

  17. Classification Tree: Tree (View/Tree) (Node 3: 1 – 63.7%)

  18. Classification Tree: Multiway Split

  19. Classification Tree: Multiway Split (Max. number of branches 4)

  20. Classification Tree: Multiway Split (View/Tree)

  21. Classification Tree: Multiway Split (Min. number of observations in leaf 25, Observation required for split 100)

  22. Classification Tree: Multiway Split (View/Tree)

  23. Regression Tree

  24. Regression Tree (Chapter3_2)

  25. Regression Tree: Input Data Source (WAGES)

  26. Regression Tree: Input Data Source (LNWAGE  Target)

  27. Regression Tree: Data Partition (70:30:0)

  28. Regression Tree: Insight (Data set)

  29. Regression Tree: Insight (Insight based on: Entire data set)

  30. Regression Tree: Insight (Run)

  31. Regression Tree: Insight (분석/분포, lnwage  Y)

  32. Regression Tree: Insight (분석/분포, 평균(1.875), 표준편차(0.545), 분산(0.297))

  33. Regression Tree: Tree

  34. Regression Tree: Tree (Results)

  35. Regression Tree: Tree (보기/통계량)

  36. Regression Tree: Tree (View/Tree)

  37. Interactive Learning

  38. Classification Tree (Chapter3_1)

  39. Interactive: Tree (Interactive …)

  40. Interactive: Tree Desktop Application

  41. Interactive: Split Node

  42. Interactive: Edit Rule…

  43. Interactive: Edit Rule…

  44. Interactive: Edit Rule…

  45. Interactive: First Split

  46. Interactive: Train

  47. Interactive: Train

  48. Variable Selection

  49. Classification Tree (Chapter3_1)

  50. Selection: Tree (Score, Data) (Training, Validation, and Test)

More Related