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characteristics that make them more suitable for particular tasks. For instance, in Gradient Boosted Decision Trees, the weak learner is always a decision tree. In Stochastic Gradient Boosting, Friedman introduces randomness in the algorithm similarly to what happens in Bagging. At each iteration,