Default Regularization for Sklearn's Logistic Regression

Requirements Clarification & Assessment

• Understanding the Context: The question revolves around the default regularization technique used in Sklearn's logistic regression model. Regularization is a crucial concept in machine learning, particularly in linear models, to prevent overfitting.

• Key Concepts:

  • Regularization: A technique used to prevent overfitting by adding a penalty term to the loss function.
  • Types of Regularization: L1 (Lasso) and L2 (Ridge) are the most common types.
  • Sklearn's Logistic Regression: A popular implementation for logistic regression in Python's Scikit-learn library.

• Interview Objective: The interviewer aims to assess the candidate's understanding of regularization, its importance, and specific implementation details in Sklearn.

• Additional Considerations: It may be beneficial to explore how different regularization techniques can impact the model's performance and when to choose one over the other.