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I have been working on ensemble learning and I came across this doubt that unlike other ensemble learning algorithms like voting classifier a can we only use one classifier with boosting.

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Boosting typically only use one algorithm as it's base learner (almost exclusively decision trees). However, you could use a mixed set of algorithms as your base learners.

Something like this:

Boosting round 0: Add decision tree
Boosting round 1: Add neural network
Boosting round 2: Add KNN
Boosting round 3: Add decision tree
...

The reason you only see boosting using the same algorithm is probably just because it works better. I speculate that the diversity that comes from using several algorithms shine more when they are trained in parallel and combined. In boosting the base learners are trained in a sequence.

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