Choose Models for Imbalanced Data and Time-Series Forecasting

Q: Choose Models for Imbalanced Data and Time-Series Forecasting

This question evaluates competence in selecting and tuning models for time-series forecasting with trend and seasonality and for extremely imbalanced classification, while assessing understanding of OLS assumptions, trade-offs among tree ensemble methods, imbalance handling approaches, and end-to-end forecasting workflows.

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Question

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

Ordinary Least Squares (OLS): Explain how OLS linear regression works and list its key assumptions.
Tree Ensembles: Compare gradient-boosted trees, random forests, and bagging. When would you prefer each?
Class Imbalance (0.2% positive): How would you handle this imbalance during model training and evaluation?
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.

Choose Models for Imbalanced Data and Time-Series Forecasting

Scenario

Tasks

Hint

Solution

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Choose Models for Imbalanced Data and Time-Series Forecasting

Overview

Scenario

Tasks

Hint

Solution

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