Ensembles: Bagging, Forests, Boosting, Voting, and Stacking
This module helps learners understand why ensembles often perform well and what they cost.
Browser lab plus download fallbackRead the lessons, open the module notebook in the browser lab when available, and keep the downloadable pack for a local copy. Write the algorithm decision evidence before chasing scores.
Notebook options
Use the browser lab for quick practice, or extract the ZIP and open modules/module-07/notebooks/module-07-algorithm-notebook.ipynb locally. The downloadable pack remains the fallback if browser storage, network, or device limits interrupt the lab.
Module sequence
- Bias, variance, and diversity in practical languageUnit 1 · 25-40 minutes
- BaggingUnit 2 · 25-40 minutes
- Random forests and randomized treesUnit 3 · 25-40 minutes
- Gradient boosting and histogram gradient boostingUnit 4 · 25-40 minutes
- AdaBoost at a high levelUnit 5 · 25-40 minutes
- Voting and stackingUnit 6 · 25-40 minutes
- Early stopping, learning rate, depth, and leakage-safe tuningUnit 7 · 25-40 minutes
- Project step: ensemble comparisonUnit 8 · 25-40 minutes
- Activity: Create an ensemble comparison notePractice activity · 45-75 minutes
- Module 7 Assessment: Ensembles: Bagging, Forests, Boosting, Voting, and Stacking CheckModule check · 30-45 minutes