Week 10 - Ensembles
Learning Objectives
After today's class, you should be able to:
- Understand the techniques for ensemble learning by applying both meta-learning approaches and prediction combination methods to improve model performance and generalization.
- Implement voting and stacking as well as out-of-the-box ensemble models in Python using sklearn.
- Develop custom scorer functions and build pipelines that handle preprocessing and modeling steps.
- Be able to apply hard and soft voting classifier predictions manually.
Class Agenda
- Ensembles Slides
- Ensembles Tutorial
[5 min]Break (Optional)- In Class Time for CP4_Ensembles
Task List
- Required Reading: Chapter 13 - Generating, Comparing, and Combining Multiple Models
- Complete and Submit CP4_Ensembles on Canvas.
- Start working on PA4_Credit_Card_Attrition_P1
- Start working on GP1_Project_Proposal