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I have an idea of how ADABOOST will be used for classification but I want to get the idea of how to re-weight and thus use ADABOOST in case of regression problems.

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Here are link to some famous boost of regressor.

Scikit-Learn have many implementations:

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As a general principle Adaboost builds and ensemble by sequentially adding members which have been trained on those instances of data which are proving most difficult to correctly classify/predict.

Each new classifier/predictor is given a training set where the difficult examples are increasingly represented, this is achieved either through weighting or resampling.

There should be very little difference in the approach for classification or regression.

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AdaBoost is a meta algorithm, so the underlying principle is the same: i would suggest going through this for references.

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