Bagging and Boosting Techniques

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?