Bagging from Boosting Techniques

Requirements Clarification & Assessment

To effectively address the interview question about distinguishing Bagging from Boosting in machine learning, it is crucial to understand the specific aspects the interviewer might be interested in. Here are some key considerations:

1. Objective Understanding:

   • What is the primary goal of each technique? (e.g., reducing variance, bias, etc.)
   • How do these techniques contribute to improving model performance?

2. Mechanism of Action:

   • How do Bagging and Boosting operate? (e.g., parallel vs. sequential model training)
   • What is the role of data sampling in each technique?

3. Algorithmic Differences:

   • What are the main algorithms that utilize Bagging or Boosting? (e.g., Random Forest, AdaBoost)
   • How are models weighted and combined in each approach?

4. Use Cases and Suitability:

   • In what scenarios is Bagging preferred over Boosting and vice versa?
   • What are the limitations of each method, and how do they address different types of model deficiencies (e.g., overfitting, underfitting)?

5. Technical Depth:

   • How deep should the explanation go? (e.g., high-level overview vs. detailed algorithmic steps)
   • Are there specific examples or case studies that should be included to illustrate the differences?