What is regularization (L1 vs L2)?
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🔹 What is Regularization?
Regularization is a technique in machine learning used to reduce overfitting by adding a penalty to the loss function. Overfitting happens when a model learns noise or irrelevant patterns from training data, performing poorly on unseen data.
Regularization discourages the model from fitting overly complex functions by shrinking the size of model weights (coefficients).
🔹 Types of Regularization
1. L1 Regularization (Lasso)
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Adds the absolute value of weights to the loss function:
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Encourages sparsity: some weights become exactly zero.
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Good for feature selection, since irrelevant features are eliminated.
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Example: Lasso Regression.
Adds the absolute value of weights to the loss function:
Encourages sparsity: some weights become exactly zero.
Good for feature selection, since irrelevant features are eliminated.
Example: Lasso Regression.
2. L2 Regularization (Ridge)
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Adds the squared value of weights to the loss function:
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Encourages small weights but rarely zero.
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Helps distribute importance across features instead of eliminating them.
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Example: Ridge Regression.
Adds the squared value of weights to the loss function:
Encourages small weights but rarely zero.
Helps distribute importance across features instead of eliminating them.
Example: Ridge Regression.
🔹 Key Differences
Aspect L1 (Lasso) L2 (Ridge) Penalty term (\sum w_i Effect on weights Shrinks some to zero Shrinks but not zero Feature selection Yes (selects subset of features) No Stability Less stable if features are correlated More stable with correlated features Use case When many irrelevant features exist When all features may matter
| Aspect | L1 (Lasso) | L2 (Ridge) |
|---|---|---|
| Penalty term | (\sum | w_i |
| Effect on weights | Shrinks some to zero | Shrinks but not zero |
| Feature selection | Yes (selects subset of features) | No |
| Stability | Less stable if features are correlated | More stable with correlated features |
| Use case | When many irrelevant features exist | When all features may matter |
👉 In short:
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L1 (Lasso) → Makes models simpler by eliminating irrelevant features.
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L2 (Ridge) → Keeps all features but reduces their influence to prevent overfitting.
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Sometimes, a mix of both is used (Elastic Net).
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