Databricks Machine Learning Associate Question 395
Select 3A data scientist is training a machine learning model to predict customer churn. They want to improve the model's accuracy and reduce overfitting. They decide to use an ensemble learning method. Which of the following statements about ensemble learning are true?
- A
Ensemble learning combines predictions from multiple models to improve performance.
- B
Bagging reduces model variance by training multiple models on different subsets of the training data.
- C
Boosting primarily works by reducing bias through sequentially training models on errors made by previous models.
- D
Ensemble methods are always better than using a single model, regardless of the problem.
- E
Random Forest is an example of an ensemble learning algorithm that uses boosting.
Show answer and explanation
Correct answers: A, B, C
Explanation
Ensemble learning is a technique that combines the predictions of multiple models to improve accuracy and robustness. Bagging and boosting are two popular ensemble methods: bagging reduces variance by training multiple models on different subsets of data, while boosting reduces bias by focusing on errors made by previous models. Random Forest is an example of a bagging method, and it differs from boosting-based techniques like Gradient Boosted Trees.
- A. Correct.
Correct: Ensemble learning improves model performance by combining the predictions of multiple models, leveraging their diversity to achieve better generalization.
- B. Correct.
Correct: Bagging, such as in Random Forest, reduces variance by training models on different subsets of the training data and averaging their predictions.
- C. Correct.
Correct: Boosting reduces bias by sequentially training models, where each subsequent model focuses on correcting the errors of the previous ones.
- D. Incorrect.
Incorrect: Ensemble methods are often effective, but they are not guaranteed to always outperform single models, especially in cases of insufficient data or poorly tuned models.
- E. Incorrect.
Incorrect: Random Forest is an example of a bagging-based ensemble learning method, not boosting.