2. Predictive Analytics
Build time-valid forecasts and probabilities that can support a defined decision
2. Predictive Analytics
Predictive analytics estimates an unknown future or withheld outcome from information available at a specified origin. Its core promise is out-of-sample performance, not explanation of why the world works.
Learning outcomes
After this module, you can:
- construct train, validation and test sets that respect time and entity dependence;
- compare regression and forecasting models with credible baselines;
- evaluate classification ranking, calibration and subgroup errors;
- explain what trees and ensembles add and what they obscure;
- choose thresholds using consequences and capacity;
- monitor data, concept, calibration and policy drift.
Chapter route
| Chapter | Question | Output |
|---|---|---|
| Validation and Leakage | What would have been knowable at the prediction origin? | evaluation protocol |
| Regression and Forecasting | How large is the future quantity or conditional mean? | benchmarked forecast |
| Classification and Calibration | How well do scores rank and quantify risk? | probability audit |
| Trees and Ensembles | Does nonlinear structure improve future performance? | complexity comparison |
| Decision Metrics and Thresholds | Which score should trigger action? | cost/capacity threshold |
| Explainability and Drift | Can the system be understood and remain valid? | model card and monitor plan |
Prepare, work, follow up
- Prepare: draw the timeline of one historical prediction row.
- Workshop: find leakage, fit a baseline and compare two thresholds under different costs.
- Follow up: write a model card that includes the no-model alternative and rollback rule.
All code has a static calculation beside it. Students may audit supplied outputs rather than execute code.