Frequently Asked Questions

Explain the Difference Between L1 and L2 Regularization?

Both L1 and L2 Regularization are used to prevent overfitting by penalizing the coefficients or weights. Below are the differences between L1 and L2 Regularization.

L1 Regularization L2 Regularization
Lasso Regression Ridge Regression
Adds the absolute value of the coefficient as a penalty term to the loss function. Adds the squared value of the coefficient as a penalty term to the loss function.
Penalizes the sum of the absolute value of the weights. Penalizes the sum of squares of weights.
Performs Feature Selection. Does not Perform Feature Selection.
Robust to outliers. Not robust to outliers.
L = ∑(Ŷi– Yi)² + λ∑|β| L = ∑(Ŷi– Yi)² + λ∑β²

 

 

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