Reviewing an existing predictive model for operational issues and performance gaps.

A model treats calendar month as a continuous variable.  What problems can this cause and how would you fix them? Why is standardizing predictors important before fitting certain models, and what might go wrong if you skip it? Your training data are highly imbalanced.  Describe two ways to adjust the loss function or evaluation metrics so recall is properly rewarded.

Discuss cyclic features, scale sensitivity, weighted loss, focal loss, precision-recall trade-off.

Google Data Scientist interview question: Identify and Fix Predictive Model Performance Gaps. {"blocks": [{"key": "0006af1b", "text": "Scenario", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {...