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  • Analogy: Consult several doctors, based on a combination of weighted diagnoses—weight assigned based on the previous diagnosis accuracy
  • How boosting works?
    • Weights are assigned to each training tuple
    • A series of k classifiers is iteratively learned
    • After a classifier Mi is learned, the weights are updated to allow the subsequent classifier, Mi+1, to pay more attention to the training tuples that were misclassified by Mi
    • The final M* combines the votes of each individual classifier, where the weight of each classifier's vote is a function of its accuracy
  • Boosting algorithm can be extended for numeric prediction
  • Comparing with bagging: Boosting tends to have greater accuracy, but it also risks overfitting the model to misclassified data

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