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Weka

Learn how to open, edit, and visualize data files using Weka preprocessing. Explore techniques for visualizing pairs of variables and handling missing values. Discover methods for discretizing data with equal frequency proportional k-interval discretization. Evaluate variable rankings using mutual information filter and CFS. Understand supervised classification paradigms and assess performance using classification trees (ID3, J48), K-NN, and Bayesian classifiers (Naive Bayes).

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Weka

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  1. Adapted from: Prof. Pedro Larrañaga Technical University of Madrid Weka

  2. Preprocess

  3. Open a file

  4. Edit a file

  5. Visualize a variable

  6. Visualizing pairs of variables

  7. Missing values

  8. Discretize with Equal Frequency

  9. Proportional k-intervaldiscretization

  10. FSS: Ranking Variables using Mutual Information

  11. Filter FSS: CFS

  12. Supervised Classification Paradigms

  13. Assesing performance

  14. Assesing performance

  15. Classification Trees: ID3, J48 (C4.5)

  16. ID3

  17. ID3

  18. J48 (C4.5)

  19. J48 (C4.5)

  20. K-NN = Lazy

  21. IB1

  22. IBk

  23. Bayesian classifiers

  24. Naive Bayes

  25. Naive Bayes

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