Regularization

Regularization is a technique in machine learning and statistics used to prevent overfitting by adding a penalty term to the loss function, which discourages overly complex models. It helps improve model generalization to unseen data by constraining the magnitude of model parameters, such as weights in linear regression or neural networks. Common forms include L1 regularization (Lasso), L2 regularization (Ridge), and Elastic Net, which combine both L1 and L2 penalties.

Also known as: Regularisation, Reg, Penalization, Shrinkage, Weight decay

Why learn Regularization?

Developers should learn regularization when building predictive models, especially in scenarios with high-dimensional data or limited training samples, to avoid overfitting and enhance model robustness. It is essential in applications like image classification, natural language processing, and financial forecasting, where accurate generalization is critical. Regularization techniques are widely used in frameworks like scikit-learn, TensorFlow, and PyTorch to optimize model performance.

Learning Resources

• docs: Regularization in Machine Learning - Scikit-learn Documentation
• course: Regularization for Deep Learning - DeepLearning.AI Course
• video: L1 and L2 Regularization Explained - StatQuest Video
• book: Regularization Methods for Machine Learning - Book by Trevor Hastie et al.