Regularization in Machine Learning

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.