Databricks Machine Learning Associate Question 401
Select 3You are tasked with building an ensemble machine learning model to classify customer churn in a Databricks environment. You want to maximize performance by combining the strengths of multiple base models. Which of the following statements correctly describe the differences between bagging, boosting, and stacking?
- A
Bagging reduces variance by training multiple models independently on different subsets of the data and averaging their predictions.
- B
Boosting reduces bias by sequentially training models, where each subsequent model focuses on the errors of the previous one.
- C
Stacking combines base models by training a meta-model on their predictions, which helps improve generalization.
- D
Bagging typically uses a weighted combination of predictions from multiple models to emphasize the most accurate ones.
- E
Boosting and bagging both aim to reduce variance by training multiple models independently on the same dataset.
Show answer and explanation
Correct answers: A, B, C
Explanation
Bagging, boosting, and stacking are ensemble techniques that combine multiple models to improve predictive performance. Bagging focuses on reducing variance through independent training on bootstrapped subsets, boosting reduces bias by sequentially addressing errors, and stacking improves generalization by using a meta-model to combine predictions. Understanding these differences is critical when choosing an ensemble method based on the problem's requirements.
- A. Correct.
Correct: Bagging, short for bootstrap aggregating, trains multiple models independently on different subsets of data (created using bootstrapping) and averages their predictions to reduce variance and improve stability.
- B. Correct.
Correct: Boosting sequentially trains models where each subsequent model focuses on correcting the errors of the previous one, which helps reduce bias and improve predictive accuracy.
- C. Correct.
Correct: Stacking involves combining the predictions of base models by training a meta-model on those predictions. This approach leverages the strengths of diverse models to improve generalization.
- D. Incorrect.
Incorrect: Bagging averages the predictions of the models rather than using a weighted combination. Weighted combinations are more characteristic of boosting, where weights are adjusted for each model.
- E. Incorrect.
Incorrect: While both boosting and bagging use multiple models, boosting focuses on reducing bias through sequential training, whereas bagging reduces variance by training models independently.