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In Adaboost, when you reweight the samples, how does the training process for the next weak learner in the boosting algorithm take in to account the weights? Is it reflected in the loss function of the next weak learner? The ESL book doesn't really talk about this, and I was a bit confused on how the weights manifest itself in the actual learning process for the next model to be added.

In addition, if say we are using trees as the weak learners, how is each subsequent tree determined? Is there a preset random selection of variables to be considered for partitioning like in random forest?

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