I've been reading and searching information about different types of Ensemble learning methods however I am a bit confused and want to make sure my understanding is correct.

Below is graph of how I understand ensemble learning methods. Is it correct?

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Also I have some questions:

  1. for Bagging and Pasting do we train different subsets of data with all same models? can we use different models for each subsets?
  2. Does Boosting use whole dataset?
  3. Is there hard voting/ soft voting in regression problems? or do we just average output of models?

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