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?
Also I have some questions:
- for Bagging and Pasting do we train different subsets of data with all same models? can we use different models for each subsets?
- Does Boosting use whole dataset?
- Is there hard voting/ soft voting in regression problems? or do we just average output of models?