Week 12: Other Techniques — imbalanced and time-series data

Learning Objectives

• 12A I can identify class imbalance in a dataset and apply techniques such as resampling or adjusted decision thresholds to address it.
• 12B I can engineer features for time-series data (lag features, rolling statistics) and use time-aware train-test splits to avoid data leakage.

Perspectival Reading

Reading: TBD

Reflection Questions

1. Imbalanced datasets often reflect a world where certain events are rare but high-stakes. What is lost when we “balance” them artificially?
2. Who typically occupies the minority class in socially consequential ML problems (fraud detection, medical diagnosis)?
3. Time-series models are trained on the past to predict the future. What assumptions does that embed about how the world changes?

Materials

• Monday class
• Wednesday class