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Data Interview Question

Regularization in Machine Learning

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Requirements Clarification & Assessment

  1. Understanding of Regularization:

    • Grasp the fundamental concept of regularization in the context of machine learning.
    • Identify the problems regularization is designed to solve, specifically overfitting and underfitting.
  2. Types of Regularization:

    • Familiarity with different types of regularization techniques, such as L1 (Lasso), L2 (Ridge), and Elastic Net.
  3. Application:

    • Knowledge of how regularization is applied in different machine learning models, including linear regression and neural networks.
  4. Mathematical Insight:

    • Understanding the mathematical formulation of regularization techniques and how they impact the loss function.
  5. Practical Implications:

    • Awareness of the practical implications of regularization on model performance and generalization ability.