Explain L1 and L2 regularization.

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L1 and L2 Regularization

🔹 Why Regularization?

In machine learning, models (especially complex ones) can overfit, meaning they perform very well on training data but poorly on unseen data.
Regularization is a technique to prevent overfitting by adding a penalty term to the loss function, discouraging overly complex models.

🔹 L1 Regularization (Lasso Regression)

• Adds the absolute value of weights as a penalty:
  Loss = Original Loss + λ Σ |wᵢ|

• Effect:

  • Encourages sparsity → many weights become exactly zero.

  • Good for feature selection (automatically removes irrelevant features).

• Example use: High-dimensional data (like text classification) where only a few features are truly important.

🔹 L2 Regularization (Ridge Regression)

• Adds the squared value of weights as a penalty:
  Loss = Original Loss + λ Σ (wᵢ²)

• Effect:

  • Shrinks weights smoothly but rarely makes them zero.

  • Spreads influence across all features.

  • Helps reduce variance without removing features entirely.

• Example use: When all features are somewhat useful but need to be controlled to avoid overfitting.

🔹 Key Differences

Aspect	L1 (Lasso)	L2 (Ridge)
Penalty	Absolute values (	w
Effect on Weights	Some become exactly zero (feature selection)	Weights shrink but rarely reach zero
Model Complexity	Produces sparse models	Produces smooth, less extreme weights
Use Cases	Feature selection, high-dimensional sparse data	General overfitting control, collinearity handling

🔑 Interview Punchline

“L1 regularization (Lasso) penalizes absolute weight values and drives some weights to zero, making it useful for feature selection. L2 regularization (Ridge) penalizes squared weight values, shrinking weights but keeping them nonzero, which helps reduce overfitting while retaining all features. In practice, both are often combined in Elastic Net.”

Read more:

What is label encoding?

What is regularization in ML?

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