Bagging Algorithm

• Let N be the number of hypotheses
• Let T be the training set
• For each hypothesis
  • Create a training set with |T| examples, selected at random, with replacement
  • Train the hypothesis using the set
  • Add the hypothesis to the ensemble
• To select a label for a test example
  • For each label L
    • Count the hypotheses that return L for the test example
  • Return the label with the highest count