l1 Regularization, also known as Lasso (Least Absolute Shrinkage and Selection Operator), adds the absolute values of the coefficients as a penalty term to the loss function. This method is particularly useful for creating models with a small number of significant features, making it ideal for applications where feature selection is crucial. In nanotechnology, l1 regularization can help in identifying the most influential variables affecting the properties of nanomaterials.
