L2 Regularization
Machine Learning
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, also known as Lasso regularization, adds the sum of the absolute values of weights as a penalty to the loss function. This encourages sparse solutions by shrinking some weights exactly to zero, effectively performing feature selection. It helps prevent overfitting and simplifies the model, making it more interpretable and efficient, especially in high-dimensional datasets.
Use Cases
- •Eliminates irrelevant features to create a simpler, more interpretable model with better generalization on high-dimensional datasets.
Related Terms
Other terms in the Machine Learning category