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Regularization: Techniques and Applications

📑 10 slides 👁 36 views 📅 1/28/2026
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Introduction to Regularization

Regularization prevents overfitting by adding penalty terms to model complexity.

Introduction to Regularization
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Advantages of Regularization

  • Reduces overfitting by penalizing large coefficients.
  • Improves model performance on unseen data.
  • Helps in feature selection (especially L1 regularization).
Advantages of Regularization
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General Rule of Regularization

  • Add a penalty term to the loss function to constrain model parameters.
  • Balance between fitting data and keeping model simple.
  • Lambda (λ) controls regularization strength.
General Rule of Regularization
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Regularization in Linear Regression

  • L2 (Ridge) adds squared magnitude of coefficients to loss.
  • L1 (Lasso) adds absolute magnitude, can zero out coefficients.
  • Elastic Net combines L1 and L2 penalties.
Regularization in Linear Regression
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Regularization in Logistic Regression

  • Similar penalties applied to logistic regression coefficients.
  • Helps prevent overfitting in classification tasks.
  • Improves model interpretability by reducing feature weights.
Regularization in Logistic Regression
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L1 vs L2 Regularization

  • L1 creates sparse models by zeroing some coefficients.
  • L2 shrinks coefficients uniformly, rarely zeros them.
  • Choice depends on need for feature selection vs. small weights.
L1 vs L2 Regularization
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Visualizing Regularization Effects

  • Regularization paths show coefficient changes with λ.
  • Validation curves display model performance vs. λ.
  • Helps in selecting optimal regularization strength.
Visualizing Regularization Effects
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Practical Applications

  • Used in high-dimensional data like genomics or text.
  • Essential in deep learning to prevent overfitting.
  • Common in recommendation systems and signal processing.
Practical Applications
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Choosing Regularization Parameters

  • Cross-validation helps select optimal λ.
  • Grid search or random search for parameter tuning.
  • Domain knowledge guides penalty type selection.
Choosing Regularization Parameters
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Conclusion and Summary

  • Regularization improves model generalization and interpretability.
  • Different types suit different problems and data structures.
  • Essential tool in modern machine learning workflows.
Conclusion and Summary
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