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
(next)