Explain L1 vs L2; ridge vs lasso
Quick Overview
Evaluates understanding of regularization in linear models—specifically the differences between L1 and L2 penalties and their expression as Lasso and Ridge regression, with attention to objective formulations and typical effects on coefficients.
Upstart
Nov 29, 2025, 12:00 AM
Data Scientist
Onsite
Machine Learning
2
0
Explain and compare:
- L1 vs L2 regularization in linear models.
- Ridge vs Lasso regression (objective functions, typical behavior of coefficients).
- Practical implications: feature selection/sparsity, stability under collinearity, optimization properties, and how you would choose/tune the regularization strength in practice.
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