Katkat
Back

Explain SHAP and build an ML project

MicrosoftData Scientist · Technical Screen
EasyData ScientistTechnical ScreenMachine Learning

Last updated

This question tests your conceptual understanding of SHAP values for model interpretability and your ability to design a robust, end-to-end machine learning pipeline for a real-world business problem.

Part 1: Conceptual Understanding

Explain the intuition behind SHAP (SHapley Additive exPlanations) values. Specifically, how do they assign credit to features in a way that satisfies the property of 'consistency'? Contrast SHAP with simpler interpretability methods like 'Permutation Feature Importance' or 'Gain/Gini Importance' used in tree-based models.

Part 2: End-to-End System Design

You are tasked with building a churn prediction system for a subscription-based streaming service. The goal is to identify users likely to cancel their membership in the next 30 days to trigger proactive retention offers.

Design the end-to-end pipeline covering the following:

  1. Data Strategy: Describe how you would handle data collection, labeling (what defines a churn event?), and potential label leakage.
  2. Modeling: Propose a model architecture and explain how you would incorporate SHAP values during the model validation phase to ensure the model isn't relying on spurious correlations (e.g., a 'support ticket' flag that only appears after a cancellation process starts).
  3. Deployment & Monitoring: How would you operationalize the model? Define two specific metrics you would monitor in production to detect data drift or concept drift.

Constraints & Considerations

  • Assume 10 million active users.
  • Assume the model must provide batch scores daily.
  • Discuss the trade-off between model complexity and interpretability in this context.

Hints

Hint 1

SHAP values are based on game theory; think about how the average marginal contribution of a feature across all possible subsets of features defines the final SHAP score.

Hint 2

For the churn project, focus on the 'observation window' and 'prediction window'—ensure your feature engineering only utilizes data available prior to the 'event horizon'.

Hint 3

Monitoring strategies should include both statistical drift detection (e.g., Kolmogorov-Smirnov test) and performance decay tracking (e.g., Precision-Recall AUC).

Follow-up questions

  • How would you explain a high SHAP value for a feature that is highly collinear with another feature to a non-technical stakeholder?
  • If the model performs well offline but fails to reduce churn in production, what are the potential reasons?
  • How would you handle the cold-start problem for users who signed up less than 7 days ago?