Optimize Churn Prediction: Feature Engineering and Model Selection

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Question

Optimize Churn Prediction: Feature Engineering and Model Selection

Weekly Churn Prediction (10M users): Feature Engineering, Model Choice, Explainability, and Debugging

Scenario

You own a weekly churn-prediction pipeline that trains on 10 million users. The goal is to predict who will churn so the business can target retention interventions.

Tasks

Feature Engineering
- Define the label, observation/prediction windows, and leakage controls.
- Propose key feature families and how to handle imbalance.
Model Selection and Hyper-parameter Tuning
- Describe the model development process, evaluation, and tuning strategy at this scale.
Model Choice Rationale
- Why might you favor Gradient Boosted Trees (GBTs) over Logistic Regression (LR) here?
Explainability
- Describe two techniques for explaining model outputs to non-technical stakeholders.
Production Debugging
- If recall drops by 15% week-over-week, provide a step-by-step debugging checklist.

Hints: Discuss imbalance handling, SHAP, feature drift, and offline/online parity checks.

Clarifying Questions to Ask Guidance

Clarify the task, data shape, labels, constraints, and evaluation metric.
State assumptions behind the math or modeling technique you choose.
Connect theory to practical training, debugging, and deployment implications.

What a Strong Answer Covers Guidance

Correct definitions and formulas where the prompt requires them.
A practical explanation of how the method behaves on real data.
Trade-offs, failure modes, diagnostics, and mitigation strategies.
Evaluation choices that match the product or modeling objective.

Follow-up Questions Guidance

How would noisy labels, class imbalance, or distribution shift affect the answer?
What would you monitor after deployment?
Which baseline would you compare against first?

Optimize Churn Prediction: Feature Engineering and Model Selection

Quick Overview

Optimize Churn Prediction: Feature Engineering and Model Selection

Optimize Churn Prediction: Feature Engineering and Model Selection

Weekly Churn Prediction (10M users): Feature Engineering, Model Choice, Explainability, and Debugging

Scenario

Tasks

Clarifying Questions to Ask Guidance

What a Strong Answer Covers Guidance

Follow-up Questions Guidance

Write your answer