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An AdaBoost regressor is a meta-estimator that starts by equipping a regressor on the actual dataset and adding additional copies of the regressor on the same dataset, but where the weights of instances are adjusted according to the error of the current prediction. As such, consecutive regressors look at difficult cases. This will help us compare the results and see how AdaBoost really boosts the performance of a decision tree regressor.