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Boosting

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

Speaker notes:

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