Which of the following is a benefit of using ensemble methods in model selection?
Practice Questions
1 question
Q1
Which of the following is a benefit of using ensemble methods in model selection?
They always perform better than single models
They reduce the variance of predictions
They require less computational power
They simplify the model interpretation
Ensemble methods combine multiple models to reduce variance and improve prediction accuracy, often leading to better performance than individual models.
Questions & Step-by-step Solutions
1 item
Q
Q: Which of the following is a benefit of using ensemble methods in model selection?
Solution: Ensemble methods combine multiple models to reduce variance and improve prediction accuracy, often leading to better performance than individual models.
Steps: 4
Step 1: Understand what ensemble methods are. They are techniques that combine multiple models to make predictions.
Step 2: Learn that combining models helps to reduce variance, which means it makes the predictions more stable and less sensitive to small changes in the data.
Step 3: Recognize that by using multiple models, ensemble methods can capture different patterns in the data, leading to improved accuracy.
Step 4: Realize that the overall performance of the ensemble is often better than that of any single model used alone.
