Example response for question delving into Machine Learning 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 of L1 regularization: L1 regularization, also known as Lasso regularization, adds a penalty term to the loss function that is proportional to the absolute value of the coefficients
•  Definition of L2 regularization: L2 regularization, also known as Ridge regularization, adds a penalty term to the loss function that is proportional to the square of the coefficients
•  Effect on the model: L1 regularization encourages sparsity in the model by driving some coefficients to zero, resulting in feature selection. L2 regularization, on the other hand, shrinks the coefficients towards zero without driving them exactly to zero
•  Feature selection: L1 regularization can be used for feature selection as it tends to set irrelevant or less important features' coefficients to zero. L2 regularization does not perform feature selection but rather reduces the impact of all features
•  Model interpretability: L1 regularization can lead to a more interpretable model as it selects a subset of features. L2 regularization does not provide feature selection, making the model less interpretable
•  Computational complexity: L1 regularization is computationally more expensive than L2 regularization due to its non-differentiability at zero. L2 regularization has a closed-form solution and is computationally efficient
•  Robustness to outliers: L1 regularization is more robust to outliers as it can completely ignore their influence by setting their coefficients to zero. L2 regularization is less robust to outliers as it only reduces their impact
•  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 generally a good default choice
•  Combining L1 & L2 regularization: L1 and L2 regularization can be combined in an elastic net regularization, which provides a balance between feature selection and coefficient shrinkage

  What the Interviewer is trying to find out about you and your experiences through this question

•  Technical knowledge: Understanding of regularization techniques in machine learning
•  Problem-solving skills: Ability to choose the appropriate regularization technique based on the problem at hand
•  Critical thinking: Analyzing the trade-offs between L1 and L2 regularization and their impact on model performance

  How to avoid some common minefields when answering this question in order to not raise any red flags

•  Confusing or incorrect explanation: Avoid providing a vague or incorrect explanation of L1 and L2 regularization. Make sure to clearly differentiate between the two regularization techniques
•  Lack of understanding of the impact: Do not overlook the importance of explaining the impact of L1 and L2 regularization on the model. Show that you understand how each regularization technique affects the model's complexity and feature selection
•  Inability to discuss use cases: Avoid being unable to discuss real-world use cases where L1 or L2 regularization would be beneficial. Demonstrate your understanding of when and why each regularization technique is commonly used

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