Compare Regularization Techniques and Their Use Cases
Overview
This question evaluates understanding of model evaluation metrics (precision and recall), regularization techniques (L1, L2, L0, L∞), and regression fundamentals including ordinary least squares assumptions and contrasts between linear and logistic regression, assessing competencies in statistical reasoning, model selection, and interpretation of predictive models. Commonly asked in the Machine Learning domain for data scientist roles to probe reasoning about evaluation trade-offs, effects of regularization on coefficients and optimization, and the conceptual versus practical implications of model assumptions, it examines both conceptual understanding and practical application.
Technical Phone Screen: Model Evaluation, Regularization, and Regression Basics
Instructions
Answer the following, focusing on clarity and practical intuition suitable for a predictive analytics/data science interview.
Questions
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Precision and Recall
- Define precision and recall using TP, FP, FN.
- When would you prioritize precision vs. recall? Give brief examples.
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Regularization Comparison
- Compare L1, L2, L0, and L∞ regularization: effect on coefficients, optimization properties, and common use cases.
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Linear Regression Assumptions
- List the key assumptions behind ordinary least squares (OLS) linear regression.
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Model Formulas and Contrast
- Write the formulas for linear regression and logistic regression, including the link functions and typical loss functions.
- Contrast the two models in terms of outputs, assumptions, estimation, and evaluation.
Solution
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