Databricks Machine Learning Associate Question 393
Select 3A data scientist is building a machine learning model to predict customer churn. They decide to use ensemble learning to improve the model's performance. Which of the following statements correctly describes key characteristics or benefits of ensemble learning?
- A
Ensemble learning combines predictions from multiple models to improve accuracy and reduce overfitting.
- B
Bagging is an ensemble method that focuses on reducing variance by training multiple models on different subsets of the data.
- C
Boosting works by training models sequentially, where each subsequent model corrects the errors of the previous one.
- D
Ensemble models are guaranteed to outperform any single model in all scenarios.
- E
Random Forest is an example of a boosting technique designed to reduce bias.
Show answer and explanation
Correct answers: A, B, C
Explanation
Ensemble learning is a powerful approach that combines the strengths of multiple models to improve prediction accuracy and robustness. Bagging reduces variance by training models on different subsets of data, while boosting reduces bias by sequentially correcting errors. However, ensemble methods are not always guaranteed to outperform single models and need to be carefully applied.
- A. Correct.
Correct. Ensemble learning improves model performance by aggregating predictions from multiple models, which helps reduce overfitting and increase accuracy.
- B. Correct.
Correct. Bagging, such as Random Forest, reduces variance by training multiple models on randomized subsets of the data and averaging their predictions.
- C. Correct.
Correct. Boosting, such as Gradient Boosting, trains models sequentially, with each model focusing on correcting the errors made by the previous models.
- D. Incorrect.
Incorrect. While ensemble methods often outperform single models, they are not guaranteed to do so in all scenarios. Factors like poor base models or inappropriate ensemble techniques can lead to suboptimal results.
- E. Incorrect.
Incorrect. Random Forest is a bagging technique, not a boosting technique. Boosting methods, like AdaBoost or Gradient Boosting, aim to reduce bias.