Databricks Machine Learning Associate Question 394
Select 3A data scientist is using ensemble learning to improve the predictive performance of their machine learning model. Which of the following statements correctly describe the basic concepts of ensemble learning?
- A
Ensemble learning combines predictions from multiple models to reduce overfitting and improve generalization.
- B
Bagging and boosting are common ensemble learning techniques used to create stronger models.
- C
Ensemble learning always reduces the training time of machine learning models.
- D
Each model in an ensemble must use the same algorithm and hyperparameters for consistency.
- E
Random Forest is an example of an ensemble learning technique that uses decision trees.
Show answer and explanation
Correct answers: A, B, E
Explanation
Ensemble learning is a powerful technique that combines predictions from multiple models to achieve better performance than individual models. Methods like Bagging and Boosting create robust models by leveraging diversity among base learners. Random Forest is a popular example of an ensemble technique. However, ensemble learning often increases computational cost, and diversity among models is encouraged for better results.
- A. Correct.
Correct: Ensemble learning aims to combine predictions from multiple models (also known as base learners) to reduce overfitting, improve generalization, and achieve better predictive performance.
- B. Correct.
Correct: Bagging (e.g., Bootstrap Aggregating) and Boosting (e.g., Gradient Boosting) are widely used ensemble learning methods that create stronger models by combining weaker learners in different ways.
- C. Incorrect.
Incorrect: Ensemble learning techniques typically increase training time since multiple models need to be trained. However, they improve model performance, which is generally the goal.
- D. Incorrect.
Incorrect: Models in an ensemble do not need to use the same algorithm or hyperparameters. In fact, combining diverse models often improves the ensemble's performance.
- E. Correct.
Correct: Random Forest is a well-known ensemble learning technique that constructs multiple decision trees during training and aggregates their predictions to improve accuracy and stability.