Advanced Modeling Ensemble Methods: Boosting
About
Understand bagging vs. boosting ensemble models, explore boosting’s impact on bias-variance, compare AdaBoost and gradient boosting, and learn to build and evaluate models like XGBoost.
Learning Objectives
You will be able to:
- Understand the differences between bagging and boosting.
- Learn the pros and cons to using boosting models.
- Learn the effect of boosting on the bias-variance trade-off.
- Learn the math and procedure for AdaBoost, a classic boosting model.
- Understand the differences between AdaBoost and gradient-boosting models.
- Build an XGBoost model.
Content
| Topic | Skills |
|---|---|
| Slides | - Conceptual overview of boosting |
| Ensemble Methods: Boosting | - Walkthrough of boosting models using scikit-learn- Bonus: Walkthrough of how to build AdaBoost from scratch in Python |
Prerequisites
- Construct individual decision trees.
- Describe how CART models are built.
- Differentiate between classification and regression problems.
- Understand the bias-variance tradeoff.
- Explain how Random Forests work.
References
đź“– Reference Materials
Important note
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