What are the methods of regularization? The pros and cons of each, areas of application.

Main regularization methods in machine learning:

1. L1 Regularization (Lasso)

   • Adds the sum of absolute weights to the loss function.
   • Leads to sparse models (many weights become zero).
   • Pros: helps in feature selection, simplifies the model.
   • Cons: can be unstable with highly correlated features.
   • Application area: when interpretability and feature selection are important.

2. L2 Regularization (Ridge)

   • Adds the sum of squared weights to the loss function.
   • Smooths weights without zeroing them out.
   • Pros: reduces overfitting, stabilizes the model.
   • Cons: does not lead to sparsity, all features remain.
   • Application area: when stability and generalization are important.

3. Dropout (for neural networks)

   • Randomly "turns off" neurons during training.
   • Pros: prevents overfitting, improves generalization.
   • Cons: increases training time, requires parameter tuning.
   • Application area: deep neural networks.

4. Early stopping

   • Stops training when validation performance deteriorates.
   • Pros: simple and effective regularization.
   • Cons: requires monitoring and validation data.
   • Application area: any iterative training models.

5. Data augmentation

   • Creates new training examples from existing ones.
   • Pros: improves generalization, reduces overfitting.
   • Cons: not always applicable, requires domain knowledge.
   • Application area: computer vision, text processing.

The choice of method depends on the task, model, and available data.