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Bagging: Boostrap Aggregation

  • Analogy: Diagnosis based on multiple doctors’ majority vote
  • Training
    • Given a set D of d tuples, at each iteration i, a training set Di of d tuples is sampled with replacement from D (i.e., bootstrap)
    • A classifier model Mi is learned for each training set Di
  • Classification: classify an unknown sample X
    • Each classifier Mi returns its class prediction
    • The bagged classifier M* counts the votes and assigns the class with the most votes to X
  • Prediction: can be applied to the prediction of continuous values by taking the average value of each prediction for a given test tuple
  • Accuracy
    • Often significantly better than a single classifier derived from D
    • For noise data: not considerably worse, more robust
    • Proved improved accuracy in prediction


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