Mini toolsDefinition
A regularization technique that adds the absolute value of weights as a penalty term to the loss function, promoting sparsity in the model.
Detailed Explanation
L1 Regularization (Lasso) adds the sum of absolute values of the weights to the loss function, effectively forcing some weights to become exactly zero. This property makes it useful for feature selection and creating sparse models, as it inherently performs feature selection by completely eliminating less important features.
Use Cases
Feature selection, Sparse model creation, Preventing overfitting in high-dimensional data
