Google Cloud Professional Machine Learning Engineer — Question 323

Your company's business stakeholders want to understand the factors driving customer churn to inform their business strategy. You need to build a customer churn prediction model that prioritizes simple interpretability of your model's results. You need to choose the ML framework and modeling technique that will explain which features led to the prediction. What should you do?

Answer options

• A. Build a TensorFlow deep neural network (DNN) model, and use SHAP values for feature importance analysis.
• B. Build a PyTorch long short-term memory (LSTM) network, and use attention mechanisms for interpretability.
• C. Build a logistic regression model in scikit-learn, and interpret the model's output coefficients to understand feature impact.
• D. Build a linear regression model in scikit-learn, and interpret the model's standardized coefficients to understand feature impact.

Correct answer: C

Explanation

The correct answer is C because logistic regression provides clear insight into feature impact through its output coefficients, making it easily interpretable. Options A and B involve complex models that prioritize accuracy over interpretability, complicating the understanding of feature contributions. Option D, while interpretable, does not align with the typical approach for binary classification like churn prediction, which is better suited for logistic regression.