Modeling

Modeling plays a critical role in machine learning system design, with classification and regression being the most frequently encountered problem types in interviews. Here are the core points from this lesson:

• Understand various evaluation metrics, such as precision, recall, F1 score, NDCG, and loss metrics like cross-entropy and focal loss, and know when to apply each
• Be prepared to discuss trade-offs between metrics, particularly precision and recall, and avoid over-optimizing for a single metric
• Recognize the significance of loss metrics, memorizing the equations for cross-entropy and focal loss, and understanding their applications in balanced and imbalanced datasets
• Be ready to discuss model selection, including architecture choices like two-tower models or matrix factorization, and anticipate follow-up questions on optimization techniques and troubleshooting issues like overfitting and vanishing gradients
• Consider a baseline model for comparison if no current system exists, and if there is an existing system, use it as a reference point

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