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Data Interview Question

Bagging and Boosting Techniques

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Requirements Clarification & Assessment

When addressing the question of when to choose bagging over boosting, it is essential to clarify and assess several aspects:

  1. Objective of the Model:

    • Are we aiming to reduce variance or bias?
    • Is the primary goal to achieve high accuracy or to ensure model robustness and generalization?
  2. Dataset Characteristics:

    • What is the size of the dataset? Is it large or small?
    • Is the dataset balanced or imbalanced?
    • Are there any specific computational constraints or resources available?
  3. Performance Requirements:

    • Is there a need for fast training and inference times?
    • Are there specific constraints on model interpretability?
  4. Operational Considerations:

    • How critical is it to avoid overfitting in the given application?
    • Is parallel computation feasible and beneficial in this context?