L1 Regularization
L1 regularization, also known as Lasso regularization, is a technique in machine learning that adds a penalty term to the loss function equal to the absolute value of the magnitude of model coefficients. It encourages sparsity in the model by driving some coefficients to exactly zero, effectively performing feature selection. This helps prevent overfitting and improves model interpretability by identifying the most important features.
Developers should use L1 regularization when building models with many features, especially in scenarios where feature selection is crucial, such as in high-dimensional data (e.g., genomics, text analysis) or when interpretability is a priority. It is particularly effective in linear models like linear regression and logistic regression, as well as in neural networks, to reduce complexity and enhance generalization to unseen data.