Baselines and honest splits
Use baselines and honest splits to move the evaluation production brief toward a defensible release.
An ETA model improves RMSE while increasing missed-delivery cost.
This lesson isolates baselines and honest splits as one decision inside that system. The people affected are product users and operators; the learning data must carry event time, availability time, ownership, and version; and the operating envelope is a declared latency, cost, and operator-capacity budget.
Choose whether and how to use censoring at a declared prediction cutoff.
Measure decision utility alongside calibration, slice reliability, and system latency—not model score alone.
An ETA model improves RMSE while increasing missed-delivery cost. An unsafe release must degrade to a named baseline or the last known-good version.
Build the mental model before the machinery.
The core move is to treat baselines and honest splits as a contract between data, a computation, and an action. Store the decision contract, cutoff, split logic, threshold policy, uncertainty, and experiment design beside the model. The implementation becomes easier to debug once you can state which inputs exist, which state is learned, what output means, and what must remain invariant after serialization.
censoring
Define it in a hand-checkable form and name the prediction-time inputs.
temporal splits
Connect it to the production metric and identify what it cannot guarantee.
PR curves
Stress it with a slice, a temporal boundary, and a failure-safe alternative.
calibration
Stress it with a slice, a temporal boundary, and a failure-safe alternative.
Lessons 1, 2 in this course.
Name every symbol. Check every shape.
Cost-aware threshold selection is the central invariant for this lesson. The formula is useful only when its inputs match the production cutoff and its output maps to an action.
Cost-aware threshold selection
| Symbol | Meaning / shape / unit |
|---|---|
t | decision threshold |
FN, FP | error counts at t |
c_FN, c_FP | business cost per error |
Open derivation and numerical substitution
Start from the production quantity being optimized, substitute the observed values with their declared units, then isolate the model-controlled term. Preserve shape annotations at each step so broadcasting or aggregation cannot silently change the result.
- Write the named inputs: t, FN, FP, c_FN, c_FP.
- Substitute one small, hand-checkable batch before vectorizing.
- Calculate an independent reference value and compare within a declared tolerance.
# equation → code contract
inputs = validate_shapes_and_units(batch)
value = compute_c02(inputs)
assert is_finite(value)Calculate it small. Shape it realistically. Break it on purpose.
A result you can reproduce on paper
Review a transaction only when expected prevented loss exceeds the review cost.
- Write every input and unit.
- Substitute values into the cost-aware threshold selection equation above.
- Compare the result to one simple baseline and explain the direction of the difference.
The same reasoning under real constraints
Choose a fraud-review threshold under reviewer capacity, false-decline, and loss constraints; verify calibration by slice.
The production record includes the data snapshot, transformation state, artifact identity, cutoff, score, decision, and the version of the policy that consumed it.
The attractive result you should reject
ROC-AUC alone on 0.1% prevalence hides unusable precision and an impossible review queue.
Diagnostic: replay the smallest failing slice from immutable inputs, then compare each boundary rather than retuning the model.
Change one assumption and make the tradeoff visible.
This lab runs predefined TypeScript only. It never executes learner code. Use the slider, numeric input, reset, live text, or table—the computation is the same.
Cost-aware threshold
Move the cutoff over a fixed set of scored transactions.
Assumption: Fixed 12-event dataset; false positive costs 2 units and false negative costs 12.
Open nonvisual data table
| Item | Computed state | Interpretation |
|---|---|---|
| 0.92 | fraud | review |
| 0.84 | fraud | review |
| 0.76 | legit | review |
| 0.68 | fraud | review |
| 0.59 | legit | review |
| 0.51 | fraud | review |
| 0.43 | legit | pass |
| 0.37 | legit | pass |
| 0.31 | fraud | pass |
| 0.22 | legit | pass |
| 0.14 | legit | pass |
| 0.08 | legit | pass |
Trace the complete operating path.
- 01
Validate and version censoring.
- 02
Compute baselines and honest splits from prediction-time-safe inputs.
- 03
Persist model, feature, and configuration identities together.
- 04
Serve or materialize behind explicit a declared latency, cost, and operator-capacity budget.
- 05
Join telemetry to mature outcomes and retain a rollback path.
Observability
Join service health, input quality, prediction distributions, slice behavior, and mature outcomes by exact version.
Cost
Measure storage, preprocessing, compute, queueing, and human review under a representative arrival pattern.
Failure modes
ROC-AUC alone on 0.1% prevalence hides unusable precision and an impossible review queue. Add a detector, owner, mitigation, and stop condition for this class of failure.
Alternatives
Compare a rule, a simpler statistical baseline, and a different system boundary before adding model complexity.
Explain the contract, not just the vocabulary.
Late-delivery evaluation contract
Specify a cutoff-safe target, validation design, cost/capacity threshold, calibration report, uncertainty, and controlled experiment.
- Decision and metric contract
- Leakage-safe split tests
- Threshold and calibration analysis
- A/B guardrails and rollback
Read primary material with a purpose.
Return to the opening failure.
Store the decision contract, cutoff, split logic, threshold policy, uncertainty, and experiment design beside the model.
For this lesson, the release evidence is a hand-checked formal result, deterministic simulation output, a ≥80% checkpoint, the production rubric, and a named fallback. The course resolves when the system can produce a metric contract, leakage-safe validation, calibration analysis, and experiment plan.