Databricks Machine Learning Associate Question 396
Select 3A data scientist is building a fraud detection model on Databricks and wants to improve its performance by using ensemble learning. Which of the following are true about ensemble learning techniques?
- A
Ensemble learning combines predictions from multiple models to improve overall performance.
- B
Bagging is an ensemble method that reduces variance by training multiple models on different subsets of the data.
- C
Boosting focuses on correcting the errors of weak learners by assigning higher weights to misclassified observations.
- D
Ensemble learning is only applicable to supervised learning problems.
- E
Random Forest is an ensemble technique that uses boosting to improve model accuracy.
Show answer and explanation
Correct answers: A, B, C
Explanation
Ensemble learning leverages multiple models to improve prediction accuracy and reduce error. Key techniques include bagging, which reduces variance by training models on bootstrapped datasets, and boosting, which improves weak learners by focusing on difficult-to-predict observations. It is not limited to supervised learning, and the Random Forest algorithm specifically employs bagging, not boosting.
- A. Correct.
Correct. Ensemble learning improves predictive performance by aggregating the results of multiple models, leveraging their combined strength.
- B. Correct.
Correct. Bagging (Bootstrap Aggregating) reduces variance by training models on bootstrapped subsets and averaging their predictions.
- C. Correct.
Correct. Boosting focuses on iteratively improving weak learners by correcting their errors, often by re-weighting data points.
- D. Incorrect.
Incorrect. Ensemble learning can be applied to both supervised and unsupervised learning problems, though it is more commonly used in supervised contexts.
- E. Incorrect.
Incorrect. Random Forest is an ensemble technique based on bagging, not boosting, where multiple decision trees are trained on subsets of the data and their results are aggregated.