Module 7: Ensemble Methods

This module covers ensemble learning techniques—combining multiple models to achieve better predictive performance than any single model alone.

What You’ll Learn¶

• Why single models fail and how ensembles help

• Voting and bagging as foundational techniques

• Random Forests for tabular data

• Gradient Boosting and its variants

• Feature importance and model interpretation

Slides¶

📊 View Lecture Slides

Lecture Materials¶

• 📝 Lecture Notes (PDF)

• 🧮 Math Notes (PDF)

• 📓 Chapter notebook: Combining Different Models for Ensemble Learning

Prerequisites¶

• Basic Python and pandas

• Familiarity with train/test splits

• Understanding of overfitting vs underfitting

Reference¶

Raschka & Mirjalili, Machine Learning with PyTorch and Scikit-Learn, Chapter 7.