Practice Disadvantages - 7.2.5 | 7. Ensemble Methods – Bagging, Boosting, and Stacking | Data Science Advance
Students

Academic Programs

AI-powered learning for grades 8-12, aligned with major curricula

Professional

Professional Courses

Industry-relevant training in Business, Technology, and Design

Games

Interactive Games

Fun games to boost memory, math, typing, and English skills

Disadvantages

7.2.5 - Disadvantages

Enroll to start learning

You’ve not yet enrolled in this course. Please enroll for free to listen to audio lessons, classroom podcasts and take practice test.

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is one disadvantage of bagging?

💡 Hint: Think about what bagging helps to reduce.

Question 2 Easy

How does bagging affect computational time?

💡 Hint: Consider how many models are trained in this process.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

Bagging is particularly effective for high-variance models.

True
False

💡 Hint: Think about the type of models bagging is used with.

Question 2

What is the primary disadvantage of bagging?

It increases bias
It requires training multiple models
It only works with specific algorithms

💡 Hint: Consider the process of how many models are created.

Get performance evaluation

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

If you are tasked with improving a highly biased model, results have shown poor performance. Would bagging be the suitable technique? Justify your reasoning.

💡 Hint: Reflect on what biases mean in terms of model performance.

Challenge 2 Hard

Imagine you have scalable computing resources, but time is tight. Discuss how bagging might fit into your model training process and the benefits or drawbacks it could entail.

💡 Hint: Consider the trade-off between time and accuracy.

Get performance evaluation

Reference links

Supplementary resources to enhance your learning experience.