Compare bagging, boosting, random forests, and bias-variance
Company: Qube
Role: Software Engineer
Category: Machine Learning
Difficulty: hard
Interview Round: Technical Screen
You are asked several ML theory questions:
1. **Bagging vs. boosting**
- What is the difference between bagging and boosting?
- When would you prefer one over the other?
2. **Random forest**
- What is a random forest?
- How does it relate to bagging and decision trees?
3. **Bias–variance tradeoff**
- Define bias and variance.
- How do bagging/boosting/random forests typically affect bias and variance?
- What common symptoms would you observe (e.g., underfitting/overfitting) and how would you mitigate them?
Quick Answer: This question evaluates understanding of ensemble methods (bagging, boosting, random forests) and the bias–variance tradeoff, assessing competency in statistical learning theory and model generalization within the Machine Learning category.