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Choose Models for Imbalanced Data and Time-Series Forecasting

AmazonData Scientist · Technical Screen
HardData ScientistTechnical ScreenMachine Learning

Last updated

Choose Models for Imbalanced Data and Time-Series Forecasting evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Scenario

You are asked to choose and tune models for forecasting marketplace demand and detecting fraud in a highly imbalanced dataset.

Question

Explain how ordinary least squares linear regression works and state its key assumptions. Compare gradient-boosted trees, random forests, and bagging; when would you prefer each? Your positive class is 0.2 % of the data. How would you handle this imbalance during model training and evaluation? Describe a full workflow for building a time-series forecasting model when seasonality and trend are present.

Hints

Cover data preprocessing, feature engineering, resampling/weighting, proper metrics, and cross-validation for temporal data.