Model Selection 10 — Exploring the Impact of Regularization on Model Selection | by Ayşe Kübra Kuyucu | Mar, 2025

Model Selection and Evaluation — Part 10/10

Table of Contents
1. The Role of Regularization in Machine Learning
2. Key Regularization Techniques and Their Mechanisms
2.1. L1 Regularization: Concept and Impact
2.2. L2 Regularization: Concept and Impact
3. Comparing Regularization Techniques in Model Selection
4. Practical Applications of Regularization in Various Industries
5. Challenges and Considerations in Applying Regularization
6. Future Trends in Regularization Techniques

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1. The Role of Regularization in Machine Learning

Regularization is a fundamental technique in machine learning that helps improve the model’s generalization capabilities, particularly in scenarios where the training data is limited or the model is too complex. By adding a regularization term to the loss function, it penalizes the model…