Katkat
Back

Identify and Fix Predictive Model Performance Gaps

GoogleData Scientist · Technical Screen
MediumData ScientistTechnical ScreenMachine Learning

Last updated

Evaluates predictive-model audit skills across month encoding, feature scaling, and imbalanced classification. Strong answers fix cyclical seasonality, standardize where needed, and adjust losses or thresholds for recall.

Scenario

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

Question

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.

Hints

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