Week 11: Other Models — logistic regression, ensembles
Learning Objectives
By the end of this week, students will be able to:
- Fit and interpret a logistic regression model for binary classification
- Explain how random forests reduce variance through bagging
- Apply gradient boosting and describe how it differs from random forests
- Choose between model families based on dataset size, interpretability needs, and performance
Perspectival Reading
Reading: TBD
Reflection Questions
- Ensemble methods sacrifice some transparency for accuracy. When is that tradeoff acceptable, and who gets to decide?
- Logistic regression outputs a probability — what does it mean to treat that number as a fact about a person?
- The dominance of certain model families in competitions shapes what gets taught and used. What gets crowded out?
Slides
Notebook Demo
Open in Google Colab (link TBD)
Lab Assignment
Week 11 Lab — GitHub Classroom (link TBD)