l1regularization
L1 regularization, also known as Lasso (Least Absolute Shrinkage and Selection Operator) regularization, is a technique used in machine learning to prevent overfitting by adding a penalty term to the cost function. This penalty is proportional to the absolute value of the magnitude of the coefficients.
The cost function with L1 regularization can be represented as: Cost = Original Cost + λ * Σ|w_i|, where Original
The key characteristic of L1 regularization is its ability to drive some coefficients exactly to zero. This
In contrast to L2 regularization (Ridge regression), which shrinks coefficients towards zero but rarely makes them