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Issues: Evaluating Classification Methods

  • Accuracy
    • classifier accuracy: predicting class label
    • predictor accuracy: guessing value of predicted attributes
  • Speed
    • time to construct the model (training time)
    • time to use the model (classification/prediction time)
  • Robustness: handling noise and missing values
  • Scalability: efficiency in disk-resident databases
  • Interpretability
    • understanding and insight provided by the model
  • Other measures, e.g., goodness of rules, such as decision tree size or compactness of classification rules

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