Google Professional Machine Learning Engineer
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You are tasked with developing a machine learning model to predict customer churn for a telecommunications company. The dataset includes customer demographics, service usage, and complaint history. The company emphasizes the importance of model interpretability to understand the factors influencing churn. Given these constraints, which step in the machine learning workflow is most critical for ensuring the selection of appropriate algorithms and techniques that align with the business's need for interpretability? Choose the best option.
Explanation:
Correct Options: D. Model selection: This is correct because selecting algorithms that prioritize interpretability, such as decision trees or logistic regression, directly addresses the business's need to understand the factors influencing churn. E. Feature engineering: This is also correct because creating meaningful features can enhance model interpretability by highlighting the most influential factors in customer churn.
Incorrect Options: A. Data collection: While important, this step does not directly influence the selection of interpretable algorithms. B. Model evaluation: This step assesses model performance but does not ensure the selection of interpretable algorithms. C. Data preprocessing: Essential for preparing data, but it does not directly contribute to selecting interpretable models.