Explain the difference between L1 and L2 regularization.
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L1 and L2 regularization are techniques used in machine learning to prevent overfitting by adding a penalty to the loss function. Both discourage overly complex models but do so in different ways.
L1 Regularization (Lasso):
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Adds the absolute value of coefficients as a penalty.
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Encourages sparsity – many coefficients become zero, effectively performing feature selection.
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Useful when you suspect only a few features are truly important.
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Penalty term: λ * Σ |wᵢ|
L2 Regularization (Ridge):
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Adds the square of coefficients as a penalty.
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Shrinks coefficients smoothly towards zero but rarely makes them exactly zero.
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Useful when most features are relevant but should be kept small to avoid overfitting.
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Penalty term: λ * Σ (wᵢ²)
Key Differences:
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Effect on coefficients: L1 makes some weights exactly zero (sparse model), while L2 makes weights small but nonzero.
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Feature selection: L1 can automatically select important features, while L2 retains all features but reduces their impact.
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Stability: L2 tends to be more stable and preferred when features are correlated, while L1 may arbitrarily select one feature over another.
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Combination: Elastic Net combines both L1 and L2 to balance sparsity and stability.
👉 In short:
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L1 (Lasso): Good for feature selection, produces sparse models.
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L2 (Ridge): Good for reducing model complexity, keeps all features with small weights.
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