What is regularization in machine learning? Explain L1 and L2 regularization.

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Regularization in machine learning is a technique used to prevent overfitting, which occurs when a model learns the noise in the training data rather than the actual pattern. Regularization adds a penalty term to the model’s loss function, discouraging overly complex models by shrinking the model coefficients.

L1 Regularization (Lasso):

• Adds the absolute value of the coefficients as a penalty term to the loss function.

• Formula:

  $$\text{Loss} = \text{Original Loss} + \lambda \sum |w_i|$$

• Encourages sparsity by driving some coefficients exactly to zero.

• Useful for feature selection, as it effectively removes less important features.

L2 Regularization (Ridge):

• Adds the squared value of the coefficients as a penalty.

• Formula:

  $$\text{Loss} = \text{Original Loss} + \lambda \sum w_i^2$$

• Shrinks coefficients evenly but rarely makes them exactly zero.

• Helps prevent large weights and reduces model complexity.

Comparison:

• L1 is better when you suspect many features are irrelevant.

• L2 is better when all features contribute and multicollinearity exists.

• Elastic Net combines both L1 and L2 penalties for more flexibility.

In both cases, λ (lambda) is a hyperparameter that controls the strength of the regularization. Higher values of λ increase the penalty, leading to simpler models that generalize better to new data.

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