What you will learn
- Observable features
- Lag
- Threshold stability
- Transition risk
Observable features
Threshold classification maps measured features into categories under explicit rules. Borderline observations can change class with small data changes. Retain the underlying feature values instead of storing only the final label.
Lag
Statistical clustering groups observations according to a chosen distance or model. Cluster names are interpretations added by the researcher. A cluster does not automatically correspond to an economic mechanism or tradable state.
Threshold stability
Train transformations and classification parameters only on the allowed development data. Recomputing clusters over the full history can alter past labels using future observations, creating a misleading point-in-time record.
Transition risk
Evaluate classification stability and uncertainty. An unfamiliar observation may not fit any learned class well. A documented unknown-state response is more honest than forcing every case into a confident category.
Worked example
A clustering algorithm is refit after new data arrives and changes labels assigned to last year. If a backtest uses those revised labels as historical decisions, it may differ from what the system actually knew then.
Try it yourself
Describe how to preserve a valid historical classification record while still allowing future model updates.
Show the worked solution
Store model version, fitted parameters, feature inputs and decision-time labels. Apply a new version only from its documented activation point in prospective evaluation; distinguish retrospective reclassification from the original record.
Apply this to your course project
Evaluate a fixed regime policy without hindsight relabelling.
Keep the calculation inputs, assumptions and decisions with your work. Practical exercises are self-reviewed; the scored knowledge checks assess the questions shown, not an independent certification of practical competence.