What is the difference between L1 and L2 regularization?
Theme: Machine Learning Algorithms Role: Machine Learning Engineer Function: Technology
Interview Question for Machine Learning Engineer: See sample answers, motivations & red flags for this common interview question. About Machine Learning Engineer: Builds machine learning models and algorithms. This role falls within the Technology function of a firm. See other interview questions & further information for this role here
Sample Answer
Example response for question delving into Machine Learning Algorithms with the key points that need to be covered in an effective response. Customize this to your own experience with concrete examples and evidence
- Definition: L1 and L2 regularization are techniques used in machine learning to prevent overfitting by adding a penalty term to the loss function
- Penalty Term: L1 regularization adds the absolute value of the coefficients as the penalty term, while L2 regularization adds the squared value of the coefficients
- Effect on Coefficients: L1 regularization encourages sparsity by driving some coefficients to zero, resulting in a sparse model. L2 regularization tends to shrink the coefficients towards zero, but rarely makes them exactly zero
- Feature Selection: L1 regularization can be used for feature selection as it drives some coefficients to zero, effectively eliminating irrelevant features. L2 regularization does not perform feature selection
- Robustness to Outliers: L1 regularization is more robust to outliers as it ignores the magnitude of the errors. L2 regularization is sensitive to outliers as it considers the squared errors
- Computational Complexity: L1 regularization is computationally more expensive than L2 regularization, especially for large datasets, as it involves solving an optimization problem with an absolute value function
- Choice of Regularization: The choice between L1 and L2 regularization depends on the problem at hand. L1 regularization is preferred when feature selection is desired or when the dataset has many irrelevant features. L2 regularization is commonly used as a default choice
Underlying Motivations
What the Interviewer is trying to find out about you and your experiences through this question
- Technical knowledge: Assessing understanding of regularization techniques in machine learning
- Problem-solving skills: Evaluating ability to choose appropriate regularization techniques based on the problem at hand
- Critical thinking: Testing analytical skills to compare and contrast different regularization methods
- Awareness of trade-offs: Determining if the candidate understands the impact of L1 and L2 regularization on model complexity and feature selection
Potential Minefields
How to avoid some common minefields when answering this question in order to not raise any red flags
- Confusing L1 & L2 regularization: Mixing up the concepts or definitions of L1 and L2 regularization
- Lack of understanding of regularization: Not being able to explain the purpose or benefits of regularization
- Inability to compare L1 & L2 regularization: Not being able to highlight the differences between L1 and L2 regularization
- Lack of knowledge on impact of L1 & L2 regularization: Not understanding the effects of L1 and L2 regularization on model complexity or feature selection