Bagging and Boosting

Two main strategies to achieve ensemble learning. A very important concept when studying machine learning or deep learning.

Basic definitions

Bagging (Bagging) trains multiple subsets independently and averages them. Boosting (Boosting) increases the weight of misclassified examples and improves them step by step. The former is parallelizable, the latter is sequential.

Detailed explanation and features

Bagging (Bootstrap Aggregating)

1. Create multiple sample sets via resampling
2. Train models independently on each set
3. Integrate predictions

Parallelizable and robust against variance.

Boosting

1. Train the first model
2. Increase weights on misclassified samples
3. Train the next model (repeatedly)
4. Predict using the weighted sum of all models

Sequential and capable of learning more complex patterns.

Computational time

• Bagging: O(k × T): Parallelizable
• Boosting: O(k × T): Difficult to parallelize

Implementation considerations

Effectiveness of bagging

Effective for unstable learners (decision trees)
Less effective for stable learners (linear regression)

Boosting

Sequential nature makes parallelization difficult
Utilize GPU-compatible tools such as XGBoost

Considerations for overfitting

Boosting iteration count should be monitored using validation data

Bagging and Boosting Trends in the G-Exam

Bagging and Boosting are frequently tested topics in the G-Exam. They are explicitly mentioned in the syllabus, and questions often test basic understanding. Pay attention to the following points:

• Understand the definitions and roles accurately
• Grasp the relationships with other related concepts
• Know practical application examples
• Understand the strengths and limitations

Recommended Learning Resources

If you want to master Bagging and Boosting for the G-Exam, the following resources will be helpful.

Books

• 『Deep Learning Textbook: Deep Learning G-Exam Official Textbook, 2nd Edition』 (JDLA)
  A standard choice for G-Exam candidates. This official textbook allows systematic learning according to the syllabus. Start with this one first.

• 『Deep Learning G-Exam: The Strongest Pass Textbook [2nd Edition]』 (SB Creative)
  Fully aligned with the latest syllabus, and includes chapter-end questions and mock exam PDFs. Useful for reinforcing the official textbook.

• 『Clear Understanding of Deep Learning G-Exam Textbook & Practice Problems, 3rd Edition』 (TAC Publications)
  Rich in illustrations and easy to understand, making it suitable for beginners. Especially recommended for those with a humanities background.

• 『Deep Learning G-Exam Problem Book, 2nd Edition』 (Also known as: Red Book)
  Focused on practical problem practice. It covers the new syllabus content and is ideal for final preparation.

💡 The official books for the G-Exam can be quite expensive, but you can find used copies on a certain online marketplace at a much lower price. Since the content of exam preparation books doesn't change much, used copies are sufficient.

Web Services

• AI Certification Dot Net
  A website where you can solve practice questions for the G-Exam for free. Convenient for those who want to do problem practice on their phone during spare time.