What you will learn
- Target definition
- Decision horizon
- Class imbalance
- Label availability
Target definition
Define a target that matches a decision, such as a later return over a specified horizon or a probability of an event. The target's construction determines when its label becomes available and whether observations overlap.
Decision horizon
Decision horizon and execution horizon must be compatible. A model predicting a move too brief for the assumed order latency may have little practical value. Prediction quality and executable usefulness are separate questions.
Class imbalance
Class imbalance can make accuracy misleading. A model predicting the common class every time may score well while missing the cases that matter. Compare with simple baselines and metrics suited to the decision.
Label availability
Labels must not contain future information that leaks into features. Store each label's information interval. A training example's row date alone may not show that its target uses returns from a later evaluation period.
Worked example
If 95 of 100 examples are class zero, always predicting zero yields 95% accuracy but detects none of the five class-one cases. Whether this is useful depends on the decision, not the headline accuracy alone.
Try it yourself
Specify a baseline and at least two measurements that expose this model's limitations.
Show the worked solution
Use the always-zero baseline explicitly, report the confusion table and class-specific recall or precision as appropriate, and connect errors to decision costs. A higher score must be evaluated against the same information and sample.
Apply this to your course project
Compare a simple baseline with one model using a frozen temporal evaluation plan.
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.