Contrasting Ridge and Lasso Regression

Requirements Clarification & Assessment

When preparing for an interview question on contrasting Ridge and Lasso regression, it is crucial to understand the following:

• Purpose of the Question:

  • To assess the candidate's understanding of regularization techniques in linear regression.
  • To evaluate the candidate's ability to apply theoretical knowledge to practical scenarios, such as feature selection and multicollinearity.

• Key Concepts to Cover:

  • Definition and purpose of regularization in regression models.
  • Mathematical formulation and differences between Ridge (L2) and Lasso (L1) regression.
  • Impact of regularization on model coefficients and feature selection.

• Expected Depth of Knowledge:

  • The candidate should demonstrate a clear understanding of both techniques, their mathematical underpinnings, and practical applications.
  • Ability to articulate when and why one method may be preferred over the other in specific scenarios.

• Potential Follow-up Questions:

  • How do Ridge and Lasso regression handle multicollinearity?
  • Can you explain the role of the regularization parameter $\lambda$?
  • How would you implement these techniques in a programming language like Python?