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I am aware that an ensemble machine learning model is a stack of two or more machine learning models. Is there a word to refer to those individual models that go into the ensemble model? (i.e. a specific term/jargon?)

I wish to refer to them and am not aware of any specific terminology to describe them. I've considered:

  • "sub-models"
  • "component models"
  • even "pancake models" (since people generally understand what's being referring to when they hear 'one of the pancakes in the model')
stevec
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3 Answers3

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I am not aware of a specific definition. Wikipedia does not mention such a term either. I would prefer "components", "individual/constituent models", or something like that.

If you definitely want to find a "correct" term, one way to discover it (if it exists) is to look into an ensemble learning early paper. To arrive at one, a good way is to search for a survey paper (e.g. here) and go on a journey to the past to find that original study.

From a quick skim of the above, you might want to look at Exploratory Data Analysis (Tukey 77), if you manage to find the pdf.

Djib2011
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npit
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I have heard people calling them "weak learners" many times, but this is only when they are not very acurate themselves.

David Masip
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It depends on the ensemble model technique. If you are going to use a bagging approach then the term individual models or alternative models is the appropriate. But in the case of boosting methods the appropriate term is weak learner (classifier). Boosting algorithms is a family of machine learning algorithms that convert weak learners to strong ones. A weak learner is defined to be a classifier that is only slightly correlated with the true classification (it can label examples better than random guessing).

Christos Karatsalos
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