Choose Models for Imbalanced Data and Time-Series Forecasting

Quick Overview

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.

Choose Models for Imbalanced Data and Time-Series Forecasting

Company: Amazon

Role: Data Scientist

Category: Machine Learning

Difficulty: hard

Interview Round: Technical Screen

##### 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.

Quick Answer: 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.

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Aug 4, 2025, 10:55 AM
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Choose Models for Imbalanced Data and Time-Series Forecasting

Scenario

You must choose and tune models for (a) forecasting marketplace demand with seasonality and trend, and (b) detecting fraud where the positive class rate is only 0.2%.

Tasks

  1. Ordinary Least Squares (OLS): Explain how OLS linear regression works and list its key assumptions.
  2. Tree Ensembles: Compare gradient-boosted trees, random forests, and bagging. When would you prefer each?
  3. Class Imbalance (0.2% positive): How would you handle this imbalance during model training and evaluation?
  4. Time-Series Forecasting Workflow: Describe a full, practical workflow for modeling a series with trend and seasonality, including preprocessing, feature engineering, appropriate metrics, and time-aware cross-validation.

Hint

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

Clarifying Questions to Ask Guidance

  • Clarify the task, data shape, labels, constraints, and evaluation metric.
  • State assumptions behind the math or modeling technique you choose.
  • Connect theory to practical training, debugging, and deployment implications.

What a Strong Answer Covers Guidance

  • Correct definitions and formulas where the prompt requires them.
  • A practical explanation of how the method behaves on real data.
  • Trade-offs, failure modes, diagnostics, and mitigation strategies.
  • Evaluation choices that match the product or modeling objective.

Follow-up Questions Guidance

  • How would noisy labels, class imbalance, or distribution shift affect the answer?
  • What would you monitor after deployment?
  • Which baseline would you compare against first?
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