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Lazy vs. Eager Learning

  • Lazy vs. eager learning
    • Lazy learning (e.g., instance-based learning): Simply stores training data (or only minor processing) and waits until it is given a test tuple
    • Eager learning (the above discussed methods): Given a set of training tuples, constructs a classification model before receiving new (e.g., test) data to classify
  • Lazy: less time in training but more time in predicting
  • Accuracy
    • Lazy method effectively uses a richer hypothesis space since it uses many local linear functions to form an implicit global approximation to the target function
    • Eager: must commit to a single hypothesis that covers the entire instance space

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