Validate Features That Predict User Retention

# Validate Features That Predict User Retention You have user records with signup time and plan type, plus timestamped user events. You are asked which features might predict retention and how to establish confidence in their predictive value. Candidate ideas include event count, active days, plan type, and signup weekday. Describe an exploratory and modeling approach. Define the prediction time and retention window explicitly, explain how features are constructed without leakage, and show how you would test whether the features add useful signal. The retention definition is not fixed: make your chosen definition and assumptions explicit. ### What a Strong Answer Covers - A timeline separating available features from the future retention outcome. - Behavioral and signup features with defensible aggregation and missing-data semantics. - Held-out evaluation, cross-validation appropriate to time, and a meaningful baseline. - Ablation or held-out importance analysis, class imbalance, and limitations of predictive claims. ```hint Draw the timeline Ask whether every event used by a feature would already exist at the moment the prediction is made. ``` ### Follow-up Questions - What goes wrong if first-week event counts predict whether any event occurred in that same week? - How would you interpret a feature that has high random-forest importance but adds little held-out performance?