Choose Logistic Regression for Integrity Risk

Business Context

Meta Integrity needs a binary classifier to predict whether a newly created Facebook ad account will trigger a policy enforcement action within 14 days. You must decide when a regularized logistic regression is preferable to a tree-based model for this production use case, then build and evaluate both.

Dataset

You are given a tabular dataset of ad-account snapshots collected at account creation time.

• Size: 420K ad accounts, 36 features
• Target: Binary — enforcement within 14 days (1) vs no enforcement (0)
• Class balance: 8.7% positive, 91.3% negative
• Missing data: 12% missing in linked-entity signals, 4% missing in payment metadata

Success Criteria

A solution is good enough if it:

• achieves PR-AUC >= 0.34 on the held-out test set,
• maintains recall >= 0.70 at an operating precision of at least 0.25,
• and provides a defensible explanation for choosing logistic regression over a tree-based model in this setting.

Constraints

• Scores are used in a reviewer-assist workflow, so interpretability and calibration matter.
• Batch scoring runs every 15 minutes on fresh account creations; p95 inference should be < 20 ms per 1K rows.
• The model is retrained weekly and must be stable enough for policy and ops teams to monitor coefficient or feature drift.

Deliverables

1. Train a regularized logistic regression baseline and a tree-based benchmark.
2. Compare them on PR-AUC, ROC-AUC, recall at fixed precision, calibration, and latency.
3. Explain when logistic regression should be preferred for this Meta integrity problem.
4. Describe preprocessing, feature engineering, threshold selection, and validation strategy.
5. Recommend a production model and justify the tradeoff between accuracy, interpretability, and operational simplicity.