Explain SHAP and build an ML project

Q: Explain SHAP and build an ML project

This question evaluates understanding of model explainability using SHAP and the competency to design and operationalize an end-to-end machine learning project, covering interpretability, limitations, data collection and labeling, feature engineering, model selection and evaluation, deployment, and monitoring within the Machine Learning domain.

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Question

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Part A: SHAP

What is SHAP (SHapley Additive exPlanations) trying to measure?
How do you interpret:
- A local SHAP explanation for a single prediction?
- A global SHAP summary plot across many samples?
What are common limitations/pitfalls (correlated features, baseline choice, causality vs association, computation)?

Part B: End-to-end ML project

Describe how you would build a machine learning project end-to-end for a business use case (e.g., churn prediction, fraud detection, recommendations, demand forecasting). Cover:

Problem framing and success criteria (offline + online)
Data collection, labeling strategy, and data quality checks
Feature engineering and leakage prevention
Train/validation/test splitting strategy (time-based if needed)
Model selection, tuning, and evaluation
Deployment, monitoring (data drift + performance), and retraining strategy
How you communicate tradeoffs to stakeholders

Explain SHAP and build an ML project

Quick Overview

Part A: SHAP

Part B: End-to-end ML project

Solution

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