Regularization Overview

🟪 1-Minute Summary

Regularization adds a penalty term to the loss function to discourage complex models and prevent overfitting. Main types: L1 (Lasso) adds |coefficient| penalty, L2 (Ridge) adds coefficient² penalty. L1 can zero out coefficients (feature selection), L2 shrinks all coefficients. Elastic Net combines both. Hyperparameter λ controls strength.

🟦 Core Notes (Must-Know)

What is Regularization?

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Why Regularization Works

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Types of Regularization

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• L1 Regularization (Lasso)
• L2 Regularization (Ridge)
• Elastic Net (L1 + L2)

When to Use Regularization

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🟨 Interview Triggers (What Interviewers Actually Test)

Common Interview Questions

1. “What is regularization?”

   • [Answer: Penalty on model complexity to prevent overfitting]

2. “What’s the difference between L1 and L2?”

   • [Answer: L1 = feature selection, L2 = shrinkage]

3. “When would you use regularization?”

   • [Answer: Overfitting, multicollinearity, high-dimensional data]

🟥 Common Mistakes (Traps to Avoid)

Mistake 1: Not scaling features before regularization

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Mistake 2: Using same λ for all features

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🟩 Mini Example (Quick Application)

Scenario

[Compare no regularization vs L1 vs L2]

Solution

from sklearn.linear_model import LinearRegression, Ridge, Lasso

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🔗 Related Topics

• Ridge Regression
• Lasso Regression
• Overfitting

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