Among the machine learning algorithms, the following is not an integrated learning strategy?

Question

Options

• ABoosting
• BStacking
• CBagging
• DMarking

• A
  15% (9)
• B
  3% (2)
• C
  6% (4)
• D
  76% (47)

Explanation

Marking (D) is not an ensemble learning strategy - it's not a recognized machine learning technique at all, making it the odd one out. Boosting (A) is an ensemble method that trains models sequentially, where each model corrects the errors of the previous one (e.g., AdaBoost, XGBoost). Bagging (B) trains multiple models in parallel on random subsets of data and aggregates their predictions to reduce variance (e.g., Random Forest). Stacking (C) combines predictions from multiple base models using a meta-learner to improve overall performance.

Memory tip: The three real ensemble strategies - Bagging, Boosting, Stacking - can be remembered as "BBS" (like a bulletin board system): Bagging is parallel, Boosting is sequential, and Stacking uses a meta-model. Any option that doesn't fit one of these three patterns is a decoy.

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