Compare Regularization Techniques and Their Use Cases
Quick Overview
Evaluates model evaluation, regularization, and regression basics for predictive analytics. Strong answers define precision and recall, compare L1, L2, L0, and L-infinity penalties, state OLS assumptions, and contrast linear and logistic regression formulas and losses.
Compare Regularization Techniques and Their Use Cases
Company: Amazon
Role: Data Scientist
Category: Machine Learning
Difficulty: medium
Interview Round: Technical Screen
##### Scenario
Model evaluation and regularization choices in predictive analytics.
##### Question
Define precision and recall; when is each more important? Compare L1, L2, L0, and L-infinity regularization and give use cases. List the key assumptions of linear regression. Write the formulas for logistic regression and linear regression and contrast them.
##### Hints
Mention sparsity, overfitting control, and link functions.
Quick Answer: Evaluates model evaluation, regularization, and regression basics for predictive analytics. Strong answers define precision and recall, compare L1, L2, L0, and L-infinity penalties, state OLS assumptions, and contrast linear and logistic regression formulas and losses.
Compare Regularization Techniques and Their Use Cases
This technical phone screen asks about model evaluation, regularization, and regression basics for predictive analytics.
Constraints & Assumptions
Answer with formulas where useful, but prioritize intuition and use cases.
Discuss binary classification and regression separately when needed.
Include model-selection trade-offs, not only definitions.
Use examples where precision, recall, and regularization choices matter.
Clarifying Questions to Ask Guidance
Is the model being optimized for classification, regression, ranking, or risk scoring?
Are false positives or false negatives more costly?
Is interpretability or sparsity important?
Are features high-dimensional, correlated, noisy, or expensive to collect?
Part 1 - Precision and Recall
Define precision and recall using true positives, false positives, and false negatives. When is each more important?
What This Part Should Cover Guidance
Precision as
TP / (TP + FP)
and recall as
TP / (TP + FN)
.
Use cases where false positives are costly versus false negatives are costly.
Threshold selection and PR curves.
Part 2 - Regularization Comparison
Compare L1, L2, L0, and L-infinity regularization. Explain effects on coefficients, optimization properties, and common use cases.
What This Part Should Cover Guidance
L1 promoting sparsity and feature selection.
L2 shrinking coefficients and handling multicollinearity smoothly.
L0 as direct sparsity with difficult combinatorial optimization.
L-infinity as bounding maximum coefficient magnitude.
Bias-variance trade-offs and hyperparameter tuning.
Part 3 - Linear Regression Assumptions
List the key assumptions behind ordinary least squares linear regression.
What This Part Should Cover Guidance
Linearity, independent errors, homoscedasticity, no perfect multicollinearity, exogeneity, and normally distributed errors for exact small-sample inference.
Diagnostics and consequences when assumptions are violated.
Part 4 - Model Formulas and Contrast
Write the formulas for linear regression and logistic regression, including link functions and typical loss functions. Contrast the two models.
What This Part Should Cover Guidance
Linear regression predicts continuous outcomes with identity link and squared error.
Logistic regression predicts probabilities for binary outcomes with logistic link and log loss.
Output interpretation and thresholding for classification.
What a Strong Answer Covers Guidance
A strong answer defines metrics and models precisely, compares regularization techniques by geometry and use case, and connects assumptions and losses to practical modeling decisions.
Follow-up Questions Guidance
When would L1 hurt performance?
How would you choose a regularization strength?
Why is accuracy often insufficient for imbalanced classification?
