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Weka - PowerPoint PPT Presentation

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Weka

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Presentation Transcript

  1. Weka

  2. Preprocessing

  3. Opening a file

  4. Editing a file Visualize a variable

  5. Visualizing pairs of variables

  6. Missingvalues

  7. Discretizing with equalfrequency

  8. Proportional k-intervaldiscretization

  9. FSS: ranking variables with mutual information

  10. FSS

  11. Filter FSS: CFS

  12. Supervisedclassificationparadigms

  13. Assessing performance

  14. Assessing performance

  15. Bayesianclassifiers

  16. Naive Bayes

  17. Naive Bayes

  18. TAN

  19. TAN

  20. TAN

  21. K-NN = Lazy

  22. IB1

  23. IBk

  24. Rule induction

  25. RIPPER

  26. RIPPER

  27. Classification trees: ID3, J48 (C4.5)

  28. ID3

  29. ID3

  30. J48 (C4.5)

  31. J48 (C4.5)

  32. Logisticregression

  33. Logistic regression

  34. Combining classifiers

  35. Combining classifiers

  36. Combining classifiers

  37. Exercise All variables FSS1 FSS2 Naive Bayes TAN IB1 IBk RIPPER Id3 C4.5 Logistic

  38. Exercise All variables FSS1 FSS2 AdaBoostM1 Bagging Stacking Vote LMT NBTree RandomForest RandomTree LBR