Follow the transformation
Start from the prediction time and forbid future/target-derived information.
Define a feature whose meaning follows from domain or model needs.
Compute it identically for training and future data.
Data Preparation for ML · Lesson 17
Feature Construction
Feature construction creates new predictor variables from existing information to expose structure that a model may not represent easily in the raw fields.
Feature Construction
Feature construction creates new predictor variables from existing information to expose structure that a model may not represent easily in the raw fields. Examples include ratios, domain formulas, interactions, calendar features and aggregates computed strictly from information available at prediction time.
Learning goal: explain why Feature Construction behaves this way, apply it to a small example, and verify the result independently. Begin by being able to justify this first step: Start from the prediction time and forbid future/target-derived information.
Deeper walkthrough
Read Feature Construction as a mechanism, not a recipe
Treat this as a sequence of observable decisions rather than one opaque command. Stage 1: Start from the prediction time and forbid future/target-derived information. Stage 2: Define a feature whose meaning follows from domain or model needs. Stage 3: Compute it identically for training and future data. Final checkpoint: Evaluate the feature inside cross-validation rather than selecting it from test-set gains.
Know what would convince you
- Verify the split/validation boundary before comparing scores.
- Inspect model/preprocessing state or a hand-computable tiny example.
Useful distinctionRepresentation: How the method encodes inputs/predictions.
Click a stage to inspect what happens, what changes, and what should be checked before moving on.
Stage 1
Start from the prediction time and…
Start from the prediction time and forbid future/target-derived information. Treat the output from Feature Construction as evidence to inspect: confirm its type, shape, range or units and connect it back to the input that produced it.
Output focus: inspect both the value and its shape/type/meaning before treating it as a trustworthy result.
Trace the mechanism step by step
- Start from the prediction time and forbid future/target-derived information.
- Define a feature whose meaning follows from domain or model needs.
- Compute it identically for training and future data.
- Check missing/zero-denominator and extreme-value behaviour.
- Evaluate the feature inside cross-validation rather than selecting it from test-set gains.
Construct a rate
# Step 1 — Compute the right-hand expression and store its result in `distance_km` for the next step.
distance_km = 120
# Step 2 — Compute the right-hand expression and store its result in `time_hours` for the next step.
time_hours = 2
# Step 3 — Compute the right-hand expression and store its result in `speed_kmh` for the next step.
speed_kmh = distance_km / time_hours
# Step 4 — Display the current value explicitly so the result/state can be inspected during execution.
print(speed_kmh)Expected / illustrative result
The constructed feature is 60 km/h and has a domain interpretation that the raw pair may not expose as directly.
Interpret the result.
For Feature Construction, connect the result to the fitted state, held-out data or prediction rule that produced it and independently check one prediction, split or metric component.
Place the concept correctly
RepresentationHow the method encodes inputs/predictions.Learning/operationWhat fitted state or calculation changes.ValidationIndependent evidence used to judge generalisation or correctness.When it is appropriate
Use Feature Construction when it answers a defined question in Data Preparation for ML and its inputs/assumptions match the current data or program state.
When to stop or reconsider
Reconsider Feature Construction when the required information is unavailable, the operation would violate a validation/data boundary, or a simpler operation answers the question more transparently.
Failure modes to recognise
- Optimising on the final test set.
- Ignoring feature scale/representation or split structure when the method depends on them.
- Reporting a single score without checking errors, variance or operating conditions.
How to check the result
- Verify the split/validation boundary before comparing scores.
- Inspect model/preprocessing state or a hand-computable tiny example.
- Perturb one input/hyperparameter and predict the expected direction or behaviour.
Demonstrate understanding
Try this:
Construct a tiny example of Feature Construction. First start from the prediction time and forbid future/target-derived information. Then define a feature whose meaning follows from domain or model needs. Predict the result before execution and explain one boundary or failure case.
Use a tiny fixed split or synthetic example. State what is fitted, what remains held out, and what result you expect before running it.
Check reasoning, not memorisation
Which approach best demonstrates understanding of Feature Construction?
Remember the logic
Step 1Start from the prediction time and forbid future/target-derived information.Step 2Define a feature whose meaning follows from domain or model needs.Step 3Compute it identically for training and future data.Step 4Check missing/zero-denominator and extreme-value behaviour.What to remember
- Feature construction creates new predictor variables from existing information to expose structure that a model may not represent easily in the raw fields. Examples include ratios, domain formulas, interactions, calendar features and aggregates computed strictly from information available at prediction time.
- Start from the prediction time and forbid future/target-derived information.
- Optimising on the final test set.
- Verify the split/validation boundary before comparing scores.