Week 7: Decision trees and hyperparameter tuning

Learning Objectives

• 07A I can train and interpret decision tree models and explain how tree depth affects overfitting and underfitting.
• 07B I can use cross-validation to evaluate model performance across multiple data splits.
• 07C I can tune model hyperparameters using grid search.

Perspectival Reading

Reading: TBD

Reflection Questions

1. Decision trees are often called “interpretable” — is a tree with 50 nodes still interpretable? By whom?
2. Hyperparameter tuning optimizes a metric. What gets optimized away in the process?
3. Cross-validation gives an estimate of generalization. Generalization to what population?

Materials

• Monday class
• Wednesday class