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Weka

This guide explores the various features of Weka for data mining, including file handling, variable visualization, and techniques for preprocessing datasets with missing values and discretization. It covers feature selection strategies such as mutual information and CFS. The document delves into supervised classification paradigms, presenting various algorithms such as Naive Bayes, K-NN, RIPPER, ID3, J48 (C4.5), and logistic regression. Additionally, it discusses ensemble methods like AdaBoost, bagging, stacking, and offers exercises for practical understanding.

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Weka

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  1. Weka

  2. Preprocessing

  3. Opening a file

  4. Editing a file

  5. Visualize a variable

  6. Visualizing pairs of variables

  7. Missing values

  8. Discretizing with equalfrequency

  9. Proportional k-intervaldiscretization

  10. FSS: ranking variables with mutual information

  11. Filter FSS: CFS

  12. Supervised classification paradigms

  13. Assessing performance

  14. Assessing performance

  15. Bayesian classifiers

  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. Logistic regression

  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

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