Prediction Quality Feedback Loops

Why this matters

Prediction quality feedback loops turn model outputs into learning. In a Machine Learning Engineer role, you will:

• Log every prediction so you can measure real-world precision/recall, calibration, and business impact.
• Collect delayed ground-truth labels (days or weeks later) and correctly join them to predictions.
• Detect performance drift, recalibrate thresholds, and decide when to retrain.
• Reduce bias from actioned predictions (selection bias) using exploration and propensity-aware evaluation.
• Close the loop end-to-end: prediction → action → outcome → learning → improved model.

Who this is for

• Machine Learning Engineers building or maintaining production models.
• Data Scientists responsible for post-deployment evaluation and monitoring.
• Product engineers integrating model decisions into user experiences.

Prerequisites

• Comfort with Python or SQL for data joins and metrics computation.
• Basic understanding of classification/regression metrics (AUC, precision/recall, MAE, calibration).
• Awareness of data drift and model versioning concepts.

Concept explained simply

A prediction quality feedback loop is a system that captures what the model predicted, what actually happened, and uses that information to measure quality and improve the model.

Mental model

Think of a circular flow: Log → Label → Join → Measure → Act. If you make this circle fast, reliable, and unbiased, your model steadily improves. If any segment breaks (e.g., missing IDs, delayed labels, biased labels), you fly blind.

Architecture of a production feedback loop

1. Log predictions: Store prediction_id, entity_id (e.g., user_id, order_id), timestamp, model_version, input schema hash, prediction score/class.
2. Log actions (optional): Record whether and how the prediction was used (e.g., flagged, discounted, ranked higher).
3. Collect outcomes: Store eventual ground truth or proxy signals plus timestamps (e.g., chargeback, return, click, conversion).
4. Define label windows: Specify how long you wait for the outcome (e.g., 45-day return window).
5. Join logic: Deterministic join by prediction_id if available; otherwise by entity_id and time window with clear rules.
6. Compute metrics: Slice-aware metrics (by segment, version, time), calibration, threshold curves, business KPIs.
7. Alerts: Thresholds for degradation (e.g., recall down 20% week-over-week) and label availability SLOs.
8. Learning: Trigger recalibration or retraining; schedule backfills as late labels arrive.

Worked examples

Example 1: Fraud detection with delayed chargebacks

Task: Predict fraud on transactions. True fraud labels appear 60–90 days later via chargebacks.

• Label window: Use 90 days; compute provisional metrics at 30/60 days and finalize at 90 days.
• Join: By prediction_id; if missing, join on transaction_id with ts_event between ts_pred and ts_pred + 90d.
• Metrics: Precision@threshold, recall, PR AUC, calibration; alert if finalized recall drops >10% month-over-month.
• Bias watch: If high-risk scores trigger auto-blocks, you may never observe true labels for blocked cases; incorporate exploration or post-authorization audits.

Example 2: Recommendations using click as proxy

Task: Rank items on a homepage. You get immediate clicks but delayed purchases.

• Primary proxy: Click within session; secondary: purchase within 7 days.
• Join: Use request_id + position + item_id to attribute clicks to a specific prediction.
• Metrics: CTR calibration, expected revenue uplift; run exploration (e.g., epsilon-greedy 2–5%) and store propensities for unbiased offline evaluation.

Example 3: Credit risk with action-induced bias

Task: Approve/deny loans. If you deny, you never see repayment outcome.

• Problem: Missing-not-at-random labels create bias if you train only on approved applications.
• Mitigations: Randomized exploration band within safe ranges; use inverse propensity weighting or reject inference techniques; track policy propensities.
• Metrics: PSI drift on features, KS statistic stability, default rate calibration by score deciles.

Designing metrics and windows

• Label latency: Define clear waiting periods (t_wait). Maintain rolling metrics at t_wait and finalize later.
• Attribution: Use unique prediction_id; if not possible, define unambiguous time and entity-based joins.
• Calibration: Reliability curves and Brier score; recalibrate (e.g., Platt/Isotonic) when curves drift.
• Slices: Always compute by cohort (region, device, new/returning, version) to localize regressions.
• Business alignment: Tie to downstream KPIs (e.g., refund rate, revenue per session) while still tracking pure model metrics.

Implementation checklist

• Unique prediction_id generated and stored.
• Prediction/event schemas versioned and documented.
• Time is UTC, ISO-8601, with clear timezones.
• Label window(s) defined and encoded in code/config.
• Join logic deterministic and test-covered.
• Late-arriving labels backfilled and re-scored.
• Metrics computed per version, per slice, over time.
• Alert thresholds and runbooks documented.
• Exploration policy and propensities logged when actions affect outcomes.
• Data retention policy covers the maximum label delay.

Exercises

Do these before the quick test. The quick test is available to everyone; log in to save your progress.

1. Exercise 1 (Design): Create a feedback loop plan for returns prediction with a 45-day label window. See detailed instructions in the Exercises section below.
2. Exercise 2 (SQL join): Join predictions to outcomes and compute Brier score and calibration bins.
3. Exercise 3 (Bias): Propose a selection-bias-aware evaluation using exploration and inverse propensity weighting.

Common mistakes and how to self-check

• Missing IDs: Not logging prediction_id leads to ambiguous joins. Self-check: Pick 10 random predictions and trace to outcomes unambiguously.
• Time leakage: Joining outcomes that occurred before the prediction. Self-check: Validate ts_event ≥ ts_pred.
• Ignoring label delay: Treating unlabeled as negative. Self-check: Track label availability rate by cohort over time.
• Selection bias: Training/evaluating only on actioned cases. Self-check: Compare metrics on exploration vs policy traffic.
• Unstable definitions: Changing label definition without versioning. Self-check: Version label logic and keep a changelog.
• No slices: Looking at overall metrics only. Self-check: Add top-5 critical slices and alert on each.

Practical projects

• Build a mini feedback loop: Simulate predictions for 10k users, generate delayed labels, store logs, and compute weekly metrics and calibration curves.
• Propensity-aware evaluation: Implement epsilon-greedy ranking with logged propensities; estimate CTR with inverse propensity weighting.
• Recalibration pipeline: Detect calibration drift and apply Platt or isotonic calibration; compare Brier score before/after.

Learning path

1. Solid prediction logging and schema versioning.
2. Reliable label collection and windowing rules.
3. Deterministic joins and slice-aware metrics dashboards.
4. Alerts and runbooks for degradation and missing labels.
5. Bias-aware evaluation (exploration, propensities) and retraining triggers.

Next steps

• Integrate label-latency-aware dashboards in your monitoring system.
• Add exploration to counter selection bias where actions alter outcomes.
• Automate recalibration and define retraining SLOs tied to metric thresholds.

Mini challenge

Your model’s weekly recall dropped 15% while label availability simultaneously dropped from 92% to 70%. Decide: Is this a true quality drop, a labeling issue, or both? Outline the top three checks you would run within 30 minutes to isolate the cause.