ML Feature Lab

ML Feature Lab: practical plan

Feature design

Prediction framing

Define prediction time, churn outcome and observation window first. Every feature must be available as of prediction time. Exclude post-cancellation events and any target-derived status.

Candidate features

• Days since last meaningful activity
• Usage trend over fixed historical windows
• Number of unresolved support cases as of cutoff
• Payment failure count before prediction time
• Plan tenure and renewal timing

Leakage checks

Use point-in-time joins, temporal train/test splits and feature lineage. Inspect for fields populated only during cancellation or support save workflows.

Fairness and privacy

Assess whether location, company size or sensitive proxies are appropriate. Minimize personal data and evaluate performance across relevant segments.

Validation

Check missingness, drift, stability, predictive value and operational availability. Compare against a simple baseline and verify feature reproducibility.

Deployment

Version transformations, monitor freshness and drift, define fallback for missing events, and document owners. Do not use a score for consequential decisions without human review and governance.

Measurement and review

Record the baseline, source, date range, denominator and owner. Use one primary measure plus quality guardrails; compare like-for-like cohorts and disclose missing data. Treat estimates as scenarios, not observed outcomes.

Risks and controls

Check relevance, consent, disclosure, workload and downside before scaling. Keep an evidence trail, obtain human review for material commitments, and pause if trust, quality or compliance signals deteriorate.

Next steps

1. Confirm the goal, audience and available capacity.
2. Gather the minimum evidence for a first test.
3. Assign an owner, budget and review date.
4. Run a small pilot, record outcomes and decide whether to iterate, expand or stop.