Bagging In integrated learning, the relationship between each base learner is?

Question

Options

• AAdditive relationship
• Brelationship
• CThe latter model must be built on top of the previous model
• DIndependent

• A
  7% (2)
• B
  79% (22)
• C
  11% (3)
• D
  4% (1)

Explanation

Option B likely refers to a parallel relationship, meaning base learners in Bagging are built simultaneously and independently - each model is trained on a different bootstrap sample of the data without any knowledge of the other learners. This is the defining characteristic of Bagging (Bootstrap Aggregating): all base learners are generated in parallel, then their predictions are aggregated (typically by voting or averaging).

Why the distractors are wrong:

• A (Additive relationship) describes Boosting variants like AdaBoost, where each new learner corrects the errors of the ensemble so far - Bagging has no such sequential error-correction mechanism.
• C (Latter model built on top of previous) is the hallmark of sequential/boosting methods like Gradient Boosting, the opposite of Bagging's architecture.
• D (Independent) is actually conceptually close to correct, but the question intends B's "parallel relationship" as the more precise technical answer - independence is a property of how the parallel training works, not the full structural description.

Memory tip: Think of Bagging like packing multiple bags at once - you fill them all simultaneously and independently. Boosting is more like a relay race where each runner builds on the last. If learners run together, it's Bagging; if they run in sequence, it's Boosting.

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