Databricks Machine Learning Associate Question 398
Select 3You are working on a machine learning project and want to improve your model's performance. You decide to explore ensemble learning techniques. Which of the following statements correctly compares bagging, boosting, and stacking?
- A
Bagging reduces variance by training multiple models independently on different subsets of the data and combining their predictions.
- B
Boosting focuses on reducing bias by training models sequentially, where each model corrects the errors of the previous one.
- C
Stacking involves training base models in parallel and using a meta-model to combine their predictions.
- D
Bagging and boosting both train models sequentially, but boosting uses a weighted dataset approach.
- E
Stacking is primarily used to reduce variance in models by averaging their predictions.
Show answer and explanation
Correct answers: A, B, C
Explanation
Bagging, boosting, and stacking are distinct ensemble learning techniques. Bagging reduces variance by training models independently on bootstrapped subsets of data and combining their predictions. Boosting reduces bias by training models sequentially, with each model correcting the errors of the previous one. Stacking combines the predictions of parallel base models using a meta-model to improve generalization. Understanding these differences is critical for selecting the appropriate technique based on the problem at hand.
- A. Correct.
This is correct. Bagging (Bootstrap Aggregating) trains models independently on different bootstrapped subsets of the data, aiming to reduce variance by averaging or voting their predictions.
- B. Correct.
This is correct. Boosting trains models sequentially, with each model focusing on correcting the errors of its predecessor, effectively reducing bias and improving accuracy.
- C. Correct.
This is correct. Stacking combines predictions from multiple base models (trained in parallel) by using a meta-model that learns how to best combine those predictions.
- D. Incorrect.
This is incorrect. While boosting trains models sequentially, bagging trains models independently (in parallel), not sequentially.
- E. Incorrect.
This is incorrect. Stacking is not primarily about reducing variance; it focuses on leveraging a meta-model to combine predictions for potentially better generalization.